The optimum irrigation regime was experienced at 60% ETc  where the highest saffron and wheat nutrient (nitrogen and phosphorus) uptake, nutrient (nitrogen and phosphorus) harvest indices, nutrient acquisition and use efficiencies, corm, saffron, and grain yields and lowest nitrogen loss was achieved. Moreover, manure application indicated 12, 42, 50 and 46% lower amounts of drained water, leachate nitrate nitrogen concentration, total leached nitrogen and N losses (other than N leaching), respectively, in comparison to the urea source of nitrogen showing the lower risk of groundwater nitrate pollution. Manure application showed 9, 8 and 9% increase in the concentration of corm nitrogen, phosphorus and protein, respectively, in comparison to urea application treatment. Saffron corm and stigma yields, irrigation and economic water productivities, corm nitrogen use efficiency and saffron-plant-nitrogen-acquisition efficiency in manure application surpassed respectively by 21, 25, 20, 17, 39 and 49% compared with the chemical source of nitrogen. Intercropping showed 10, 11, 23 and 64% lower amounts of drained water, nitrate concentration in drainage water, seasonal leached nitrate and N losses (other than N leaching), respectively compared to saffron sole cropping which reduces the risk of groundwaters nitrate contamination. For all the experimental treatments, empirical regression models were derived for estimation of seasonal leached nitrate based on the seasonal drained water. Intercropping saffron with winter wheat, application of organic cow manure and adopting irrigation regime of 60% ETc is an innovative system of saffron production which mitigates the risk of groundwater nitrate contamination and increases irrigation and economic water productivities. Saffron growers can make sustainable and clean use of the inter-row spaces of the saffron crop to grow winter wheat in order to obtain higher economic water productivity and lower groundwater nitrate pollution, and it is highly recommended to maintain a sustainable environment.The ever-rising trend of nitrate leaching from the agricultural production systems is a major risk to the contamination of ground- and surface-waters and should be addressed. But so far, there has been no study on the reduction of nitrate leaching from saffron fields through intercropping. Saffron growers can make a sustainable use of the saffron inter-row spaces through the strategy of winter-wheat/saffron base intercropping system to reduce nitrate leaching. During four years of study, in a set of lysimeters, effects of two cropping systems (saffron mono-cropping and saffron–wheat intercropping), application of two sources of nitrogen  and four irrigation regimes [40, 60, 80, and 100% of the standard crop evapotranspiration (ET However, excessive application of nitrogen fertilizers has disturbed the global N cycle balance, resulting in major problems in environment, health, and economics. In a global scale, near 50% of the agricultural N fertilizers cannot be efficiently taken up and utilized by plants, and is lost in the environment in the forms of nitrate (NO3−), ammonia (NH3), and nitrous oxide (N2O), which increases the costs of agricultural production and contributes to water contamination and climate change 2. In recent years, the ever-rising trend of nitrate leaching due to the excessive use of N fertilizers in the agricultural sector combined with the improper irrigation regimes in such production systems has globally increased the risk of nitrate contamination in ground- and surface-waters 4. In semi-arid regions over-irrigation is another major cause of nitrate leaching 5. Nitrate concentration in potable groundwater supplies of Shiraz city which is located in a close vicinity of the experimental cite was in the range of 5 to 72 mg N L−1 which 11% of the potable groundwater samples had nitrate concentrations above the standard level of 10 mg N L−1 for drinking water. This was as a result of excessive use of chemical N fertilizers and manure in crop production systems resulting in nitrate leaching or due to the direct or indirect negative impacts of the industrial and municipal wastewaters on groundwater resources 6. This groundwater nitrate contamination is a growing anxiety posing significant threat to individuals, society and environment in this semi-arid region.Nitrogen element is critical to crop plants by involving in their growth, leaves expansion, reproduction and biomass-yield production processes 3− leaching. The application of nitrification inhibitors has been indicated to be effective in reduction of nitrogen losses in grazed grassland ecosystems 7. Application of a nitrification inhibitor called nitrapyrin, maintained high NH4+/ NO3− ratio in soil and thus decreased leaching of NO3− in an intensive vegetable ecosystem 8. A number of studies attempted to determine the optimal value and type of nitrogen fertilizers to decrease N leaching 10. The use of some physical barrier materials, such as zeolite, was another strategy used to reduce NO3– leaching 13. Some researchers tried to decrease N leaching through irrigation methods and strategies like partial root drying irrigation and water saving irrigation strategies 14. A few studies have conducted on using plants to reduce NO3− leaching: Bergeron et al. 15 found that soil nutrient leaching reduced following the establishment of tree-based intercropping systems in eastern Canada. They found that tree roots in the tree-based intercropping system established on clay loam soil decreased subsoil NO3– leaching by 227 kg N ha−1 and 30 kg N ha−1 over two consecutive years. In another survey, pepper/maize intercropping significantly reduced NO3− leaching losses 2. However, there has been limited research on using crop plants to uptake N and reduce NO3− leaching.The above-mentioned N losses in crops production, and their environmental pollution potential has forced researchers to seek for easily adaptable agricultural management practices which can enhance N use efficiency and reduce N losses causing environmental deterioration. Some approaches have been developed to alleviate NOCrocus sativus L.), the most precious spice in the world, is mainly used as food seasoning and coloring, in perfumes, cosmetics and medical purposes. It is cultivated in Iran as a leading country in saffron production (produces 90% of the world’s saffron) and a few other countries mostly located in arid and semi-arid regions 17 that facing nitrate pollution and shortages in irrigation water resources. Therefore, recommending a sustainable approach to reduce N leaching, enhancing nitrogen efficiencies and water productivity in saffron production areas is of particular importance. This help to plummet anthropogenic environmental degradation. Sharma et al. 18 developed a new mode of N placement, i.e., ‘mid rib placement upper to corms in two splits (MRPU‑2S)’ which could decrease nitrous oxide N emission by 70% and nitrate N leaching and runoff by 68 and 67%, respectively in comparison to conventional method, in saffron soils of the northwest Himalayas. In Iran, saffron corms are commonly cultivated in rows 0.25–0.35 m apart in basins 19. According to our hypothesis, saffron growers can make a sustainable use of the common saffron inter-row spaces through the novel strategy of wheat/ saffron-base intercropping under different sources of nitrogen which may result in higher plants’ (saffron and winter wheat plants) nitrogen uptake, irrigation and economic water productivities and N efficiency beside a mitigate in N leaching loss to the environment. In this system of cropping, winter wheat can be grown in parallel rows, each located between the saffron corm rows and aligned along them. Since the depth of irrigation water is a limiting key factor in N leaching management in this semi-arid region, therefore different irrigation water levels should also be examined to find an optimum irrigation regime appropriate to this system of cropping in this region that is facing water scarcity. Therefore, in the present investigation, for the very first time, during 4 years of study, the effects of saffron mono-cropping and saffron intercropped with winter wheat under different sources of nitrogen  and different irrigation regimes  on plant nitrogen and phosphorus uptake, nitrogen leaching and nitrogen and phosphorus efficiencies were investigated in a semi-arid region of Iran (Fars province) in open-field lysimeters and their impacts on environment i.e. groundwater nitrate pollution, and total N loss were revealed. It should be noticed that in the mentioned saffron/wheat intercropping system, the amount of irrigation water for each irrigation event was calculated and adopted only based on water requirement of saffron crop and no extra water was applied for wheat crop. This gives the present study a greater importance as it provides more efficient use of irrigation water in the current water scarcity condition.Saffron  at the Experimental Research Station of the Agricultural College, Shiraz University, Iran  with the same experimental layout design in all the four growing seasons. The experimental site was located in a semi-arid region in southwest of Iran with a long-term average annual precipitation, relative humidity and air temperature of 387 mm, 52.2% and 13.4 °C, respectively. The mean monthly climatic data for the years of experiment are presented in Tables This experiment was conducted in 48 in-field water balance GRP lysimeters (100-cm inner diameter and 110 cm length)   was mixed with the soil and leveled. For the manure treatment, the top 0.15 m soil layer was supplied by 30 Mg ha–1 fermented cow manure as a source of nitrogen. The chemical characteristics of the fermented cow manure are presented in Table −120] were planted in the intercropping treatments at 5 cm soil depth in three parallel rows each located in the middle of the bare soil space between the rows of saffron corms  half of which was applied just prior to the first irrigation immediately after the sowing of winter wheat at late October and the remaining split was applied at March (approximately 120–140 days after first irrigation). Over every November of all the growing seasons, during the flowering time, fresh flowers of all the treatments were manually picked up early every morning and the three-part stigmas and styles were separated from the stamens and petals. For each replication of any treatments, all the collected stigmas and styles were shadow-air dried in room condition for about 2–3 weeks and weights were measured precisely as saffron yield. At the end of each growing season, saffron leaves, wheat grain and straw were completely harvested from each lysimeter, oven dried, weighed and divided by its harvested area to determine saffron above-ground biomass, grain and straw yields, respectively. At the end of last (fourth) growing season, saffron corms were uprooted and corm yield was measured.In all the growing seasons, in late August, the top soil of each lysimeter was deeply plowed and triple superphosphate  was measured at 0.3, 0.6 and 0.75 m of soil depths with a neutron scattering apparatus. Saffron gross water requirement (Eq. 16:g is the gross irrigation water depth (m) for irrigation regime of 100% ETc, Δzi is the soil thickness at layer i of saffron rooting depth (m), n is the number of soil layers in saffron rooting depth (RD), θFCi and θi are the volumetric soil water contents of layer i at field capacity and before irrigation (m3 m−3), respectively and Ea is irrigation application efficiency [decimal]. For the irrigation regimes of 80, 60 and 40% ETc, 80, 60 and 40% of the amount calculated in Eq.  irrigation amounts and interval and no extra water was applied for winter wheat crop. Over the periods of time with no adequate rain, irrigation interval of 24 days was adopted based on the saffron plant 24. According to this rule, 5, 6, 6 and 5 irrigation events were conducted for all the treatments in the first, second, third and fourth growing seasons, respectively. At the first growing season, in order to help a good establishment of saffron plants all the treatments were fully irrigated at the first irrigation event which was adopted in late October 2013, and afterwards the experimental irrigation regimes were carried out. Last irrigation was adopted in late April while saffron leaf senescence was initiating, and wheat plant growth continued without irrigation.Prior to each irrigation event, the volumetric soil water content  concentration was determined by spectrophotometer in less than 24 h after sampling. For each irrigation event, leached nitrate was obtained by multiplying the drainage water volume by its NO3− concentration. For each growing season, seasonal leached nitrate was obtained from the sum of leached nitrate after each irrigation or rainfall event over that growing season. Finally, seasonal leached nitrogen was expresses as a percentage of the total applied nitrogen through manure or chemical urea fertilizer.The volume of the drained water collected from the bottom of each lysimeters were measured by a volumetric container after each irrigation event and divided by the area of the lysimeter (0.79 m) to get the drainage depth. After each irrigation event, the 0.1 L samples were taken from the drainage water of each lysimeter and kept at 4 °C, and its nitrate (NOa) for the irrigation regimes was estimated through the water balance method applying Eq. (16:The plant(s) actual evapotranspiration (ETying Eq.  16:3\doc25 (Eq. Irrigation water productivity (IWP) was calculated as crop yield per cubic meter of total water use, including rainfall and irrigation water 25 Eq. .326 (Eq. −3), Y is yield (saffron dry stigmas or wheat grain) (kg ha−1), WIrr. and WRain are irrigation and rain water use, respectively (m3 ha−1), P is the yield (saffron stigmas or wheat grain) price [US$ kg-1] and EWP is economic water productivity (US$ m−3) for saffron, grain or total yields. The prices of wheat and saffron were US$ 0.38 kg−1 and US$ 1066.7 kg−1, respectively 27. Saffron and wheat grain prices were in Iranian Rials and 1US$ = 300,000 Rials 28 was used to convert the prices from Iranian Rial to US Dollar.Economic water productivity (EWP) was calculated as gross income in US$ per total water used in m326 Eq. .4\docume29. The protein concentration of plant organs  were determined through multiplying its Kjeldahl nitrogen concentration by a Kjeldahl-nitrogen-to-protein conversion factor of 6.25 30. Furthermore, the nitrate (NO3−) concentration of the drainage water was determined by spectrophotometer at 25 °C using a previously calibrated scanning spectrophotometer  set at 220 and 275 nm where the absorbance at 275 nm was taken as the background in the two wavelengths determination method of nitrate. Leachate nitrate concentration was determined using a previously prepared standard nitrate curve 31.For the last (fourth) growing season, saffron corm, saffron aboveground biomass  and aboveground biomass of the winter wheat plant (grain and straw) were oven dried at 70 °C and their total nitrogen (N) and phosphorus (P) concentration were measured according to Kjeldahl and ammonium-vanadate-molybdate methods, respectively Wheat) is defined as the ratio between nitrogen uptake in grain (NGrain) and nitrogen uptake in grain plus straw (NGrain + NStraw) multiplied by hundred 32 (Eq. Corm) and nitrogen uptake in corm plus aboveground biomass yields (NCorm + NAbbvg.) multiplied by 100 would result in saffron plant nitrogen harvest index (NHISaffron) , NGrain, NStraw, NCorm and NAbovg. are nitrogen uptake by wheat grain, wheat straw, saffron corm and saffron aboveground biomass yields, respectively (kg ha−1).For wheat plant, nitrogen harvest index (NHId 32 Eq. . By a siron) Eq. .6\documeWheat and PHISaffron are phosphorus harvest index for wheat and saffron, respectively (%), PGrain, PStraw, PCorm and PAbovg. are phosphorus uptake by wheat grain, wheat straw, saffron corm and saffron aboveground biomass yields, respectively (kg ha−1).By a simple modification, phosphorus harvest indices for wheat and saffron would be as Eqs.  and 9),,9), resp33 which addresses the nitrogen uptake by yield (grain for wheat and corm for saffron) per unit of available nitrogen in soil system . When flowering is finished at the first growing season, the daughter corms start to develop and grow on top of the mother corms. At the end of the first growing season, the color of saffron leaves change from green to yellow and development of the daughter corms is completed 34. At the following growing seasons with the aging of saffron plant, primary mother corms gradually become smaller and smaller. Hence, in this study, mother corm nitrogen content was not considered in NAE calculation for the fourth growing season. Therefore, grain and corm NAE is calculated as Eqs. (33:Grain and NAECorm are nitrogen acqisition efficiencies of grain and corm (%), respectively, NGrain and NCorm are the grain and corm nitrogen uptake (kg ha−1), respectively, NSoil and NFer. are the soil and fertilizere  available nitrogen (kg ha−1), respectively. For PAE calculations, phosphorus values have to be replaced with nitrogen in Eqs. (The nitrogen acquisition efficiency (NAE) is a soil-based nitrogen efficiency  as Eqs.  and 11)33 which 35Grain and NUtECorm are grain and corm nitrogen utilization efficiencies (kg kg−1), respectively, YGrain and YCorm are the grain and corm yields (kg ha−1), respectively, NGrain, NStraw, NCorm and NAbvg. are nitrogen uptake by whrat grain, wheat straw, saffron corm, saffron aboveground biomasses (kg ha−1), respectively. For PUtE calculations, phosphorus values have to be replaced with nitrogen in Eqs. (The nutrient (N and P) utilization efficiency  addresses the yield produced per unit of N and P, respectively, acquired (uptake) by the plant Eqs. , 13 35\dmentclasspt{minima35 as Eqs. (Grain and NUECorm are nitrogen use efficiencies of grain and corm (kg kg−1), respectively, YGrain and YCorm are the grain and corm yields (kg ha−1), respectively, NSoil and NFer. are the soil and fertilizere  available nitrogen (kg ha−1), respectively. This definition can be simply modified for phosphorus use efficiency as yield per unit of P available in soil system . For PUE calculations, phosphorus values have to be replaced with nitrogen in Eqs.   as Eqs.  and 15)35 as Eqs36:Input is the system measured nitrogen inputs including nitrogen supply in fertilizer , nitrogen from irrigation water and rain N depositions, NOutput is the measured system nitrogen outputs including crop N removal , N losses through N leaching and ∆Soil total N, is nitrogen change in soil, all in kg ha−1. In this study, SNBI shows N loses through NH4 volatilization, denitrification, gas emissions [NOx] and plant senescence which could not be determined directly in this study. Since this is a lysimetric study and the lysimeters are closed and water-sealed around systems, there would be no nitrogen loss through surface runoff and soil erosion to be taken into account in nitrogen loss calculations.The nitrogen balance index of a system (SNBI) is calculated as Eq.  36:16\doMinitab 16.2.4 statistical software was applied to determine interaction effects of irrigation regimes, sources of nitrogen and cropping systems. Analysis of variance (ANOVA) was carried out according to Tukey test to determine statistically significant differences between the means at 5% probability level. It is also confirmed that all procedures were conducted in accordance to the relevant guidelines and regulations.c. Chemical urea fertilizer decreased 20.5 and 17.5% saffron and corm yields, respectively in comparison to the manure treatments  and mean comparisons for saffron, corm, grain and wheat straw yields are presented in Table c, respectively due to the lowest and highest gross water applied. Generally, the higher the gross water depth, the greater the drained water and its percentage, and vice versa were occurred. Moreover, the urea treatment indicated a significant greater amount of drained water (11.5%) in comparison to the manure treatment owing to a higher induced soil cracks and pathways in the urea treatments which increased the risk of preferential flow and groundwater contamination. Furthermore, intercropping showed a significant lower (10.0%) amount of drained water compared to mono-cropping due to the higher amount of evapotranspiration in intercropping system (Table 40 which measured drainage water 11.0% lower in intercropped wheat and fescue than in wheat grown as a monoculture. There was a significant difference between the growing seasons on drainage water depth. The last two (third and fourth) growing seasons experienced the highest drained water from the bottom of the lysimeters in comparison to the first and second growing seasons, and this was due to the greater amount of the sum of the rainfall and gross irrigation water applied in these two years of the study  and mean comparisons for the drainage water depth and its expression as a percentage of applied gross water are presented in Table em Table . This isdy Table .Table 2Mc  due to the lowest and highest gross water applied  and mean comparisons for the average seasonal nitrate concentration in leachate  was measured in the fourth and second growing seasons, respectively, due to the highest and lowest precipitation amounts , the place of the highest and lowest nitrate concentration of the leachate changed, that is 100 and 40% ETc showed the lowest and highest leachate nitrate concentration in the manure treatments. This is again due to the higher leaching efficiency of the 100% ETc in comparison to the lower levels of irrigation treatments. Overall, the leachate nitrate concentration of manure decreased over the whole growing season due to the leaching process and crop nitrogen use, while for urea treatments, after a decreasing trend, it rose and peaked at 141 days after first irrigation due to the application of the second split of urea fertilizer just before this date . Splitting nitrogen into two applications allows the plant to utilize N more efficiently. Generally, intercropping experienced lower values of leachate nitrate concentration in comparison to mono-cropping. The same trend was observed for other growing seasons. To make the manuscript brief, only the graphs of the fourth growing season is presented and compared.The changes in nitrate-nitrogen concentration of the drainage water for the fourth growing season are illustrated in Fig. c  due to the lowest and highest volumes of applied water  and cumulative drained water (D) were derived for different treatments Table . CoefficA short summary of combined analysis of variance (ANOVA) and mean comparisons for the irrigation and economic water productivities of saffron and winter wheat grain are presented in Table c, respectively due to the minimum and maximum amounts of the applied gross irrigation water, respectively. However, 60% ETc experienced the highest wheat grain IPW since the highest grain yield was harvested from this irrigation regime  indicates how much economic outcome is produced per cubic meter of water applied. It serves as a measure of the efficiency of the used water. The trend and pattern of changes in EWP for saffron and grain yields Table  was simi45 that can improve the nutritional conditions for the growth of corms 38 and therefore, a significant increase in saffron yield. Hence, it can be concluded that the application of chemical fertilizer of urea has less effect on corm growth. These results were in line with the findings of Koocheki and Seyyedi 46 who achieved a higher concentration of corm nitrogen and phosphorus in manure compared to application of chemical fertilizer. Howbeit, nitrogen, phosphorus and protein concentration in saffron above-ground portion and wheat above-ground plant yield components (grain and straw) was not affected significantly by source of nitrogen, although a minor increase could be distinguished in the manure treatment regarding to these elements. Moreover, except for corm nitrogen and protein concentration which was affected by intercropping, there was no significant difference in N and P concentration of saffron above-ground portion and corm phosphorus in sole cropping and intercropping. The 5% decrease in both nitrogen and protein concentration of saffron corms might be a consequence of the nutrition competition of wheat rooting system.Irrigation regime did not have any significant influence on nitrogen, phosphorus Table  and prot−1, respectively, was achieved in 60% ETc, where saffron yield was relatively maximized  in comparison to farmyard manure in cotton–wheat cropping system. Moreover, intercropping lowered saffron nutrient (N and P) uptake in comparison to sole saffron due to the lower saffron nutrient concentration   Table . The higed Table  in incre46 who reported the above-mentioned indices for saffron plant in the first 2 years of its life cycle and in different corm nutrient condition  while we calculated them for the fourth year of corm life cycle with the application of 30 t ha−1 cow manure and 120 kg N ha−1 in manure and urea treatments, respectively. The greater NHI and PHI indicate directly the higher allocation of biomass nitrogen and phosphorus to grain. Indirectly, NHI or PHI indicate the way of partitioning between nitrogen or phosphorus uptake by grain and straw and NHI or PHI values allow for convenient prediction of straw nitrogen or phosphorus as grain nitrogen or phosphorus data are readily available.There was no significant difference in terms of nitrogen and phosphorus harvest indices (NHI and PHI) for saffron and wheat plants among irrigation regimes, sources of nitrogen and cropping systems Table . The valc experienced the highest nitrogen and phosphorus acquisition efficiencies, both in yield and in the whole plant  supplied by triple superphosphate compared to the manure source of nitrogen which supplied an extra 46.0 kg ha−1 available phosphorus into the soil. It should be mentioned that all the manure and chemical urea treatments were supplied by 100.0 kg P ha−1, while the manure itself supplied an extra 46.0 kg P ha−1 into the soil. This fact makes the denominator of the PUE greater in manure treatments compared to urea, and smaller grain PUE in the manure treatments is the consequence. Since the nitrogen use efficiency is a measure of efficiency of input use, it can be concluded that manure can produce higher corm yield per unit of available N and higher saffron yield is a result ] and the N removed (plant N uptake and leached nitrogen) from the soil. The values account for N losses other than N leaching, that is, N loss through denitrification, volatilization of NH4, gas emissions [NOx], plant senescence. Among the irrigation regimes, the lowest N balance value (the least nitrogen loss) was related to 60% ETc, from which the highest corm and saffron yield were harvested  irrigation and economic water productivity, plant available nitrogen in root zone and crop(s) growth. The optimum irrigation regime was 60% ETc, where the highest saffron and wheat nutrient (nitrogen and phosphorus) uptake, nutrient (nitrogen and phosphorus) harvest indices, nutrient acquisition and use efficiencies, corm, saffron, and grain yields and lowest nitrogen loss  was achieved in this irrigation regime.The lowest and highest drainage water depth, seasonal nitrate concentration of the leachate and seasonal cumulative leached nitrate was observed in 40 and 100% ETMoreover, manure as a slow-nitrogen-release fertilizer indicated 12.0, 42.0, 50.0 and 46.0% lower amounts of drained water, leachate nitrate nitrogen concentration, total leached nitrogen and system nitrogen balance index (implies nitrogen losses other than leaching), respectively, in comparison to the urea source of nitrogen which shows the lower risk of groundwater nitrate pollution. In addition, manure showed 9.0, 8.0 and 9.0% increase in the concentration of corm nitrogen, phosphorus and protein, respectively, in comparison to urea treatment. Saffron corm and stigma yields, irrigation and economic water productivities, corm nitrogen use efficiency and saffron-plant-nitrogen-acquisition efficiency in manure surpassed respectively, 21.0, 25.0, 20.0, 17.0, 39.0 and 49.0% the chemical source of nitrogen. A reversed outcome was achieved for grain and wheat plant as a whole. That is to say, irrigation and economic water productivity, nitrogen and phosphorus uptake, phosphorus-acquisition efficiency, nitrogen and phosphorus use efficiencies were higher for grain and wheat plant in chemical nitrogen application in contrast to manure source of nitrogen.Furthermore, intercropping showed 10.0, 11.0, 23.0 and 64.0% lower amount of drained water, nitrate concentration in drainage water, seasonal leached nitrate and system nitrogen balance index, respectively compared to saffron sole cropping which reduces the risk of groundwaters nitrate contamination.For all the experimental treatments, empirical regression models were derived for estimation of seasonal cumulative leached nitrate based on the seasonal cumulated drained water.c is an innovative system of saffron production which mitigates the risk of groundwater nitrate contamination. Saffron growers can make sustainable and clean use of the inter-row spaces of the saffron crop to grow winter wheat in order to obtain higher economic water productivity and lower groundwater pollution and it is highly recommended to a sustainable cropping system.To cap it all, intercropping saffron with winter wheat, application of organic cow manure and adoption irrigation regime of 60% ETSupplementary Information.


Sample homogeneity dictates whether analyzing a test portion of an entire sample can provide representative information about incurred mycotoxins. In this study, we evaluated particle-size-distribution-based homogeneity of laboratory mycotoxin samples using laser diffraction particle size analysis and International Organization for Standardization (ISO) Guide 35: 2017. Incurred whole corn, compound feed, peanut butter, and wheat flour (500 g each) were comminuted using wet, cryogenic, or dry milling. We used a sample dividing (riffling) device to obtain representative subsamples (25 g each) and developed a laser diffraction particle size analysis procedure by optimizing key parameters such as the refractive index, absorption, and stirring rate. The homogeneity of the particle size distribution within laboratory subsamples was characterized using the optimized laser diffraction procedure. An assessment of homogeneity was also performed for individual mycotoxins in each incurred matrix sample following the procedure described in ISO Guide 35. The concentrations of the incurred mycotoxins were determined using liquid chromatography–mass spectrometry (LC-MS). Within- and between-subsample variances of incurred aflatoxin B1 in peanut butter; deoxynivalenol in corn, compound feed, and wheat flour; and fumonisins in compound feed corroborated that when the particle size measurements were less than 850 µm, mycotoxins concentrations were consistent across independent test portions, which was confirmed using an analysis of variance (F-test). This study highlights the benefits of laser diffraction particle size analysis and suggests its use as a test procedure to evaluate homogeneity in new sample commodities. The determination of mycotoxins in field samples relies on sampling, sample preparation (comminution and homogenization), subsampling, and instrumental analysis . After a sample is collected, comminution by cutting, milling, grinding, and blending is typically conducted to minimize the impact of heterogenous distribution, i.e., “hot spots”, of mycotoxins . For larSample homogeneity is a challenging and underexplored issue for mycotoxin analysis, especially for LC-MS-based multi-mycotoxin analysis. With advances in LC-MS technology, especially in terms of sensitivity, conventional extraction and concentration steps have been gradually replaced by dilution ,5,6,7. SA variety of protocols have been established for performing homogeneity assessments to determine whether a predefined subsampling plan and test portion would be adequate to provide representative information on target analytes. Both ISO Guide 35: 2017  and the Sieving has been the conventional and standard sizing measurement technique for corn samples containing mycotoxins . CompareLaser diffraction particle size analysis is an alternative methodology for size-dependent homogeneity assessments. With well-established mathematical theories ,16,17 anDevelop a sizing measurement procedure for representative mycotoxin matrices using laser diffraction spectroscopy.Determine whether laser diffraction particle size analysis could provide results of the particle size distribution for homogeneity assessment in a practical and time-efficient manner.Confirm that particle size-based homogeneity assessment is consistent with ISO Guide 35.Recognizing the important intermediary role of homogeneity assessment between sample preparation and instrumental analysis, we focused on routine mycotoxin sample preparation that addressed the challenge of obtaining small but representative test portions essential for LC-MS analysis. Encouraged by the promising but limited applications of laser diffraction particle size analysis in dried fruits ,24, we cIn this study, samples were introduced into a measurement cell via a wet dispersion unit equipped with a variable speed stirrer. To achieve reliable particle size distribution results, one needs to consider key dispersion parameters such as dispersant and forces used to disperse agglomerated particles in a liquid suspension. Methanol was selected as the dispersant to wet the particle surfaces and separate touching particles . Figure Stirring is a common practice for dispersing agglomerated particles and creating a uniform suspension of particles in the dispersant. Without sufficient stirring, agglomeration, precipitation, and/or heterogenous suspension of particles could seriously influence light scattering patterns, leading to erroneous sizing data. Therefore, we compared the effect of stirring rate, ranging from 500 to 3500 rpm. To use laser diffraction technology for particle size analysis, determination of the refractive index, a complex number consisting of a real number and an imaginary number, is required. The real number is the actual refractive index of the sample; the imaginary number is defined as the absorption, which is the amount of light absorbed by the sample. An imaginary value of 0 is often assigned to a transparent sample and a value of 1.0 or greater suggests that the sample is very opaque. Generally, actual refractive index and absorption index are not available for food matrices, so empirical refractive values  and absorption values  were selected for comparison using wheat flour as the representative food matrix.The fitness of the data  and relative standard deviation in particle size distribution was used to evaluate whether the selected refractive index, 1.6, and absorption, 0.01, should be optimized. Using the Dv10, Dv50, and Dv90 generated at 1.6 and 0.01 as the benchmark, Lin’s concordance correlation coefficient (Lin’s CCC) ,28 was cThe effect of obscuration was evaluated at 10% and 20%—the low and the high ends of the normal operation range, 10–20%, recommended by the vendor. Obscuration is positively correlated with sample concentration in the measurement cell. When obscuration is set too low, with insufficient particles measured, the estimate of particle size distribution is likely to be exaggerated and variability would be high. When obscuration is set too high, with too many sample particles introduced into the measurement cell, particles are not well dispersed and distanced, leading to multi-scattering, which can change scattering patterns and skew particle size distribution results. At 20% obscuration, the only noticeable change was a 5% decrease (193 µm to 183 µm) in Dv90, while Dv10, Dv50, fitness, and Lin’s CCC remained consistent with those values at 10% obscuration.The particle size distributions  of the ten subsamples of corn, compound feed, peanut butter, and wheat flour are summarized in Whole corn kernels or compound feed had to be comminuted, as the initial sizes of kernels were much larger than 850 µm. The feed contains fibers, protein, and fat, making it difficult to form a homogenous fraction; therefore, it was prepared using cryogenic milling. This milling method achieved particle sizes with a Dv90 ranging between 522 and 613 µm. A few measurements had relatively large variability. For example, the RSDs of Dv90 in compound feed 3–2, peanut butter 1–1, 5–2, and 6–2 are >10%. Since all data points were retained without outlier analysis, sporadic large particles likely contributed to larger deviations in the measured Dv90 values. The observed within- and between-sample variability was probably also caused by the way in which test portions were taken. The sample divider cannot accurately collect 1 g aliquots, so each test portion was manually collected from the subsample and introduced into the dispersion unit. Increasing sample concentration in the measurement cell could also decrease variability at a higher probability of multi-scattering.Particle shape and surface area also could affect scattering patterns, leading to inconsistent size distributions ,33. MicrISO Guide 35  providesp > 0.05, the sample was considered homogeneous, as all the concentrations of the mycotoxin in tested subsamples were not statistically significant with the same mean. In other words, statistical analysis may be used to determine whether the sample should be further homogenized, or the results are representative for the whole sample.Following ISO Guide 35, we collected 30 test portions from ten subsamples of whole corn, compound feed, peanut butter, and wheat flour. Incurred deoxynivalenol in the test portions of whole corn, compound feed, and wheat flour; fumonisins and zearalenone in the test portions of compound feed; and aflatoxin B1 in the test portions of peanut butter were extracted and quantitated using automated sample preparation and LC-MS, followed by ANOVA to determine whether the homogeneous distribution of individual mycotoxins was achieved. If the calculated F value was less than the corresponding critical value (Fcrit) at a p > 0.05, suggesting no significant differences among the test samples. These results aligned consistently with the particle-size-based homogeneity assessment.Interestingly, fumonisin B2 failed the F test. Although variability in homogeneity, sampling, test portion size, and analytical method could contribute to the overall uncertainty in the concentration of fumonisin B2, it is unlikely that the distribution of fumonisin B2 would be significantly different from that of fumonisin B1 or B3 in the same feed product. We speculated that co-eluted interferences from the feed matrix caused the large variability due to the limited cleanup used for sample preparation . This leIn naturally contaminated agricultural commodities ,39,40, hDue to the heterogenous distribution of mycotoxins in foods, samples to be analyzed for mycotoxins are required to be comminuted prior to extraction. The required homogeneity threshold of samples is often defined by the sample particle sizes . In this work, the sample homogeneity in small-scale laboratory samples was characterized using an optimized laser diffraction particle size method and compared to ISO Guide 35: 2017. The results demonstrated that matrices with Dv90 < 850 um could be considered homogenous with respect to the analyzed aflatoxins, deoxynivalenol, and fumonisins. Although particle size analysis can only be used to estimate the distribution of individual mycotoxins based on the assumption that all incurred mycotoxins share similar distribution patterns after comminution, the methodology provides an efficient test procedure to characterize new sample commodities and confirm consistency in sample fractionation for routine analysis.Whole corn, commercial compound feed, peanut butter, and wheat flour (white) (500 g/each) were collected from local stores and used for this study. The whole corn and compound feed samples were stored at −80 °C overnight. On the next day, the two samples were mixed with dry ice and comminuted using a Retsch MM 500 mixer mill  equipped with two grinding chambers until a powdery consistency was achieved. The grinding time and speed were set at 5 min and 35 Hz, respectively. Comminuted samples were transferred into polypropylene bags and stored at −20 °C in dark conditions. After dry ice was sublimed, the samples were used for subsampling. The wheat flour and the peanut butter were mixed individually using a Blixer 4 blender for 3 min .After comminution, fractions, subsamples, and test portions  were collected via a three-step process. Step 1: Divide the 500 g sample into ten fractions of 50 g. Step 2: Divide each 50 g fraction into two subsamples of 25 g. Step 3: Collect three test portions from each of the ten 25 g subsamples for LC-MS analysis and collect two test portions from the other ten 25 g subsamples for particle size analysis. The hierarchical design of sample preparation, including the collection of fractions, subsamples, and test portions is illustrated in Step 1. The comminuted corn, compound feed and wheat flour samples were mixed to break clumps and divided into ten fractions using a Retsch PT-100 sample divider  and IKA 100 mL grinding chambers . The riffling device (sample divider) included a vibratory hopper, a feeder, and a carousel with eight collection containers. For each batch, 100 g of the sample was divided into eight aliquots. The vibration rate and rotational parameters were optimized to ensure that the relative standard deviation (RSD) of the masses of the eight aliquots (12.5 ± 0.5 g/each) was less than 5%. After the 500 g sample was divided into 40 aliquots, 4 aliquots were randomly combined and mixed in an IKA 100 mL grinding chamber. In this way, ten 50 g sample fractions (50 ± 2 g/each) were obtained.Step 2. Each 50 g fraction was divided into two subsamples (25 ± 1 g each), saved in IKA grinding chambers. This way, there were twenty 25 g subsamples for each matrix.Step 3. For corn and wheat flour, the following procedures were used to collect test portions for LC-MS analysis and particle size analysis, respectively:LC-MS test portions: We selected ten 25 g subsamples and mixed each subsample with 25 g of water following a slurry procedure ,43. The Particle size analysis test portion: The other ten 25 g subsamples were used for particle size analysis. Two test portions (1 g each) were collected from each 25 g subsample, and each test portion was measured with a particle size analyzer. Before we took the two test portions from a 25 g subsample, we slowly rotated and turned the sample tube multiple times. The first test portion was taken using a scoop soon after mixing to ensure no settling had occurred. We repeated the same steps to collect the next test portion.As it was difficult to make slurries for the compound feed, after Step 2, we selected ten 25 g subsamples and collected three test portions (1 g each) from each of the subsamples for LC-MS analysis. Prior to test portion collection, we slowly rotated and turned the sample tube multiple times. The first test portion was taken using a scoop soon after mixing to ensure no settling had occurred. We repeated the same steps to collect the next two test portions. The test portions for particle size analysis were collected from the other ten 25 g subsamples as described above. The only difference is we only collected two test portions (1 g each) from each of the subsamples for particle size analysis.The peanut butter could not be divided by the riffling device, so its ten fractions (50 g each) were prepared manually. After the first fraction was collected from the Blixer 4 blender, the remaining peanut butter was mixed using a laboratory spatula followed by the collection of the second fraction. We repeated the same procedure until the ten fractions were collected. The ten 50 g fractions were further divided into twenty 25 g subsamples. Ten 25 g subsamples were used for LC-MS analysis and three test portions (1 g each) were directly collected from each subsample without using the slurry procedure. The other ten 25 g subsamples were used for particle size analysis. Two test portions (1 g each) were collected from each subsample.The above test portions collected for LC-MS analysis were prepared as described in a previous study and an FDA compendial method ,43. In bThe test portions collected above for particle size analysis were pre-wetted using the dispersant (methanol). Transfer pipettes were used to add methanol to the samples, which were blended and pipetted up and down to ensure complete wetting. Care had to be taken to measure the sample as soon as possible after the slurry was created to eliminate any excess dissolution.Sample particle size measurements were conducted using a Mastersizer 3000 . Samples were dispersed and introduced into the Mastersizer 3000 using a Hydro LV filled with ~500 mL methanol as the dispersant. The stirrer speed was set at 3500 rpm. The particle refractive index and particle absorption index were set at 1.6 and 0.01, respectively. The refractive index of methanol was 1.32. The particle size was calculated on a volume basis using Malvern Mastersizer software (version 3.81). The analysis settings used were the General Purpose model with the Irregular Shape Mode. Background light scattering signals from the system and the dispersant (methanol) were measured prior to the addition of the sample. For the collected corn, compound feed, peanut butter and wheat flour test portions, we used transfer pipettes to add the dispersant (methanol) to pre-wet the sample and mixed the test portion into a thick slurry. We pipetted up and down to ensure complete wetting. Care had to be taken to measure the sample as soon as possible after the slurry was created to eliminate any excess dissolution. Next, individual test portions were added to the dispersion unit. We initiated the particle size measurement procedure when the obscuration value was within the manufacturer’s recommended range (10–20%). Six repeated measurements were made on each sample. Data were collected at a rate of 10 kHz. CRM3100  was analyzed as the QC sample to demonstrate instrument performance. To ensure sample particles were stable in methanol without significant morphological changes during analysis, we performed a repeatability test, measuring replicate test portions of the same wheat flour sample.To confirm sample homogeneity characterized using particle size distribution, one still needs to confirm whether one sizing threshold would be good for all analytes in different food matrices. The co-occurrence of mycotoxins in a sample matrix has been well documented; however, the question of whether homogeneity should be evaluated for each mycotoxin–matrix pair remains unanswered. To address this question, we acquired compound-specific distribution data following ISO Guide 35.To determine the homogeneity of individual mycotoxin samples, LC-MS was used to analyze test portions collected from each subsample following a previously validated LC-MS procedure . Prior tp value of >0.05, if the calculated F ratio  was less than the corresponding critical value (Fcrit), the sample was defined as homogeneous [Microsoft Excel was used to perform a one-way ANOVA of detected mycotoxins in whole corn, compound feed, peanut butter, and wheat flour. Homogeneity in the 10 subsamples of each matrix was evaluated based on ANOVA results. At a ogeneous .


We investigated cardiovascular disease (CVD) risk associated with latent tuberculosis infection (LTBI) (Aim-1) and LTBI therapy (Aim-2) in British Columbia, a low-tuberculosis-incidence setting. 49,197 participants had valid LTBI test results. Cox proportional hazards model was fitted, adjusting for potential confounders. Compared with the participants who tested LTBI negative, LTBI positive was associated with an 8% higher CVD risk in complete case data (adjusted hazard ratio (HR): 1.08, 95% CI: 0.99-1.18), a statistically significant 11% higher risk when missing confounder values were imputed using multiple imputation , and 10% higher risk when additional proxy variables supplementing known unmeasured confounders were incorporated in the highdimensional disease risk score technique to reduce residual confounding . Also, compared with participants who tested negative, CVD risk was 27% higher among people who were LTBI positive but incomplete LTBI therapy , whereas the risk was similar in people who completed LTBI therapy . Findings were consistent in different sensitivity analyses. We concluded that LTBI is associated with an increased CVD risk in low-tuberculosis-incidence settings, with a higher risk associated with incomplete LTBI therapy and attenuated risk when therapy is completed. Over 1.9 billion people are estimated to live with latent tuberculosis infection (LTBI) globally, with 10 million people developing tuberculosis (TB) disease each year . The incLTBI therapy plays an important role in TB prevention as the therapy reduces the risk of developing TB disease . BritishThe aims of this study were twofold. First, we explored whether LTBI is associated with an increased risk of CVD among people who immigrated to British Columbia, Canada. Second, we investigated the risk of CVD associated with the completion of LTBI therapy.This study is part of a larger project describing TB among foreign-born people immigrating to British Columbia . For theForeign-born persons eligible for LTBI screening include people who are in close contact with active, infectious TB, people with specific high-risk comorbidities , along with people screened as part of work and school programmes. Of note, during the study period, people were not systematically screened based on county of origin or immigration classification  . ScreeniThe binary LTBI status (positive/negative) is the exposure of interest for Aim 1. Participants were classified as LTBI positive if they tested positive on TST alone or IGRA. Participants were classified as LTBI negative if they were negative on both TST and IGRA, negative on TST alone, negative on IGRA alone, and positive on TST but negative on IGRA . We consThe exposure variable for Aim 2 was LTBI therapy, classified as LTBI negative, LTBI positive with incomplete LTBI therapy, and LTBI positive with completed LTBI therapy. A participant would have been classified as completed LTBI therapy if they received an adequate regimen of LTBI treatment as defined by the provincial TB registry .page 2 of the Supplementary Material.The outcome variable was the time from the cohort entry date to the first occurrence of a CVD event (either ischemic heart disease or stroke). The follow-up was censored at loss of medical service plan (a proxy for immigration), death, or study end (31 December 2019). The CVD events were identified from hospital separations, outpatient physician claims, and vital statistics deaths databases . The detpage 7). The following covariates were identified as potential confounders: age at immigration, sex, income, education, World Health Organization (WHO) region of birth, immigration class, smoking, alcohol use disorder, substance use disorder, hypertension, diabetes, chronic kidney disease, obesity, HIV/AIDS, and dyslipidemia [pages 2 and 3 of the Supplementary Material.All covariates were defined using a 1-year covariate assessment window, which began on the date residency in British Columbia was established and ended 365 days later. We identified potential confounders based on the literature and a causal diagram  of less than 0.2 as good covariate balancing among the exposed versus the unexposed . For theTo determine the risk of CVD associated with LTBI, we used a Cox proportional hazards model on the complete case data. The set of covariates for adjustment was identified using the modified disjunctive cause criterion that included potential confounders and risk factors for CVD but excluded instrumental variables and mediators . We adjupages 3 and 4 of the Supplementary Material. Briefly, we added the empirical/proxy variables with the covariates used in the main analysis with the assumption that empirical covariates are potentially correlated with smoking and thus can minimise bias due to unmeasured confounding by smoking [Multiple sensitivity analyses were conducted to explore the robustness of the findings. First, we conducted a missing data analysis using multiple imputation to impute missing values in covariates. We also included a ‘tobacco use’ variable from the provincial TB registry as a proxy variable for smoking status, which is an unmeasured confounder in our analysis. The tobacco use variable was excluded from the primary analysis due to a high percentage of missing values. We imputed 10 datasets using multiple imputation with multivariate imputation via chained equation. The Cox proportional hazards model was fitted on each imputed dataset, adjusting for the covariates used in the main analysis as well as tobacco use. Second, we used the high-dimensional disease risk score  to reduc smoking , 33. We  smoking . The outTo investigate the risk of CVD associated with LTBI therapy, we used a Cox proportional hazards model on the complete case data, adjusting for the same set of covariates used for Aim 1.pages 3–5 of the Supplementary Material.We repeated the above-mentioned two sensitivity analyses for Aim 2. In addition, we conducted a sensitivity analysis for the potential risk of immortal time bias. We considered a time-varying LTBI therapy exposure definition and fitted the time-dependent Cox regression, adjusting for the same set of confounders used in the main analysis. The details of the sensitivity analyses can be found on pages 5 and 6 of the Supplementary Material).We also conducted multiple complementary analyses for Aim 2. First, we used propensity score weighting analysis on a subset of the sample who had information on LTBI therapy to determine whether the observed association between LTBI therapy and CVD could be due to healthy user bias by comparing CVD rates before and after the LTBI test on the same subjects . Additionally, we conducted complementary analyses accounting for changing the exposure status for some participants due to close contact with people with TB disease and dealing with potential violations of the proportional hazards assumption .Ethical approval of the study was provided by the University of British Columbia (#H16-00265). We used the Reporting of Studies Conducted Using Observational Routinely Collected Health Data checklist for reporting this study , <1 year of follow-up , or a history of TB disease (n = 5). The size of the analytic dataset with missing covariate values was 52,593. The complete case analyses included 49,197 participants, with a total of 901,734 person-years and a median time from cohort entry to CVD event or censoring of 19 years (IQR: 13–25 years). Among these participants, 26,163 (53.2%) tested LTBI positive. The mean age at cohort entry was 19.3 years, and 59.3% of participants were female. The summary statistics among those with tested LTBI negative and positive are presented in A total of 64,801 participants had LTBI testing results . We exclThe crude CVD rate per 100,000 person-years was 246 and 224 among people who tested LTBI positive and negative, respectively. Our fully adjusted primary analysis found an 8% higher risk of CVD among participants who tested LTBI positive compared with those who tested LTBI negative  . In our n = 5,631) and no LTBI therapy information , with all SMDs <0.2 (see Supplementary Table 1 on pages 10 and 11). Among the 5,631 participants who had information on LTBI therapy, 3,202 (56.9%) completed the therapy. All covariates except for WHO birth region were approximately balanced (see Supplementary Table 2 on pages 11 and 12).The complete case analytic sample size for our Aim 2 was 28,665. The summary statistics were well balanced among those with LTBI therapy information , whereas the risk was only 4% higher if LTBI therapy was complete  . The higpage 9). In addition, results of changing the exposure status due to close contact with people with TB disease and dealing with potential violations of the proportional hazards assumption showed a similar estimate as the main analysis (see Supplementary Table 3 on page 13).Our first complementary analysis suggests that the rate of CVD was 18% higher  after discontinuation of LTBI therapy among people who did not complete LTBI therapy, versus no increment in CVD rate for those who completed the LTBI therapy compared with that before the LTBI test  rarely occurs prior to migration to Canada. With a very low likelihood of TB transmission in Canada , we coulOur study suggests that LTBI is associated with an increased risk of CVD in a low-TB-incidence setting. Moreover, our results indicate that the risk of CVD is significantly higher in people who do not complete LTBI therapy but the risk is attenuated when therapy is completed. The findings were consistent in sensitivity analyses minimising bias due to unmeasured confounding by smoking and dealing with missing covariate values and immortal time bias. With the high global prevalence of LTBI, these findings require further investigation as they may have substantial clinical and programmatic implications.


Microseismic monitoring systems (MMS) have become increasingly crucial in detecting tremors in coal mining. Microseismic sensors (MS), integral components of MMS, profoundly influence positioning accuracy and energy calculations. Hence, calibrating these sensors holds immense importance. To bridge the research gap in MS calibration, this study conducted a systematic investigation. The main conclusions are as follows: based on calibration tests on 102 old MS using the CS18VLF vibration table, it became evident that certain long-used MS in coal mines exhibited significant deviations in frequency and amplitude measurements, indicating sensor failure. Three important calibration indexes, frequency deviation, amplitude deviation, and amplitude linearity are proposed to assess the performance of MS. By comparing the index of old and new MS, critical threshold values were established to evaluate sensor effectiveness. A well-functioning MS exhibits an absolute frequency deviation below 5%, an absolute amplitude deviation within 55%, and amplitude linearity surpassing 0.95. In normal operations, the frequency deviation of MS is significantly smaller than the amplitude deviation. Simplified waveform analysis has unveiled a linear connection between amplitude deviation and localization results. An analysis of the Gutenberg–Richter microseismic energy calculation formula found that the microseismic energy calculation is influenced by both the localization result and amplitude deviation, making it challenging to pinpoint the exact impact of amplitude deviation on microseismic energy. Reliable MS, as well as a robust MS, serve as the fundamental cornerstone for acquiring dependable microseismic data and are essential prerequisites for subsequent microseismic data mining. The insights and findings presented here provide valuable guidance for future MS calibration endeavors and ultimately can guarantee the dependability of microseismic data. Microseismic events, arising as concomitant phenomena of rock mass deformation, crack initiation, and crack propagation, are earthquakes characterized by a magnitude below 3.0. These events possess less energy and a lower signal-to-noise ratio (SNR) in comparison to larger, natural earthquakes. The Microseismic Monitoring System (MMS) serves as a comprehensive record documenting the temporal and spatial attributes of microseismic events . Since iThe history of microseismic monitoring dates back to 1938 when the U.S. Bureau of Mines researched to establish a correlation between seismic wave velocity and pillar load . During Vu, GVd, and GH, installed in the roof, floor, and side of the roadway, respectively. Vertical single-component velocity MS is mainly used in coal mines, and three-component sensors are applied less. So, the MS mentioned in this paper refers to vertical single-component velocity MS. During its initial phase, China predominantly concentrated on producing complete MMS equipment. Limited attention was given to fundamental MS, with most MMS relying on established accelerometers or velocity sensors from the earthquake domain. This directly impacted the resolution, sensitivity, and reliability of domestically developed MMS systems in coal mines . AccordiBecause of the fundamental status of the MS and its importance in the MMS, the Chinese national standard puts forward the calibration requirements for the MS, requiring the calibration of the MS every two years to ensure the reliability of the MS . A comprMMS is the placement of MS in advantageous positions from which small earthquakes (microseisms) induced by some downhole process can be detected and located to provide geometric and behavioral information about the process . MMS wasThe MS utilized in the test were DLM-type MS sourced from seven coal burst-prone mines after undergoing underground usage for a minimum duration of one year. Each sensor underwent an initial screening process to verify its circuitry’s integrity and compatibility for attachment to the screws. MS calibration can be categorized into absolute and comparative methods . The absThe system connection through the comparative method is illustrated in MS are considered precision instruments, necessitating a meticulously controlled testing environment. According to the Chinese industry standard document, the construction of the concrete vibration table base must adhere to stipulated guidelines . FurtherThe experiment utilizes the CS18VLF low-frequency vibration system from SPEKTRA, a German company. −4 m/s, falling within the functional amplitude range of the DLM-type MS (1 × 10−6 m/s ~ 6.4 × 10−4 m/s). For the amplitude calibration, we held the frequency steady at 50 Hz and utilized the CS18VLF to generate six distinctive amplitudes: 6 × 10−4, 5 × 10−4, 4 × 10−4, 3 × 10−4, 2 × 10−4, and 1 × 10−4 m/s, respectively. Standard sinusoidal waveforms were chosen for both frequency and amplitude calibration; Precisely capturing the frequency and amplitude of vibration signals stands as the core function of an MS, directly influencing the reliability of an MMS. Within the operational frequency range of the DLM-type MS (0.1~200 Hz), we chose seven representative frequencies for calibration: 2, 5, 10, 20, 50, 100, and 150 Hz. The selected amplitude during the frequency calibration was 6 × 10The central steps of the calibration test comprise the following: (1) Configuring the frequency and amplitude parameters within the CS18 controller to produce specific frequency and amplitude vibrations on the APS113-AB; (2) acquiring and documenting the output value generated by the MS; (3) processing the data collected by the MS in line with the index calculation formula and subsequently comparing it with the input data.A total of 102 MS from diverse coal mines were tested under uniform environmental conditions and equipment. The waveforms captured by the MS units were subjected to Fast Fourier Transform (FFT) processing to extract the vibration frequency. The electrical signal was transformed into velocity using the transduction formula, ultimately yielding the maximum vibration velocity (amplitude).The frequency deviation index was formulated to assess the efficacy of MS during frequency calibration. This index quantifies the disparity between the measured frequency of the MS and the set frequency of the CS18VLF, thereby determining the frequency deviation. The calculation method can be described as follows:To evaluate the performance of MS in amplitude detection, two parameters have been formulated: amplitude deviation and amplitude linearity. The amplitude deviation calculation follows a similar approach to frequency deviation, making it applicable for assessing the relative accuracy of MS measurements in terms of amplitude. Drawing inspiration from the calculation method of the correlation coefficient, amplitude linearity enables the assessment of whether an MS can maintain consistent errors when capturing vibrations of varying intensities. The calculation method for amplitude deviation is defined using Equation (2), whereas the calculation method for amplitude linearity is delineated using Equation (3):Frequency calibration tests were conducted on 102 existing MS utilizing the test scheme outlined in For a more comprehensive analysis of the frequency calibration outcomes, as illustrated in −4 m/s calibration, the maximum amplitude deviation of one MS reaches −4000%. This trend could stem from the reduction in the amplitude during calibration, leading to a decrease in the denominator of Equation (2), consequently resulting in an elevation of the amplitude deviation. In total, 102 MS underwent testing on the CS18VLF, following the procedure outlined in −4 m/s, 93 MS exhibit amplitude deviations ranging from −10% to 0; during the 5 × 10−4 m/s calibration, 89 MS display amplitude deviations between −40% and −20%; t the 4 × 10−4 m/s calibration, 62 MS manifest amplitude deviations within the range of −40% to −20%; with the calibration amplitude set at 3 × 10−4 m/s, 59 MS demonstrate amplitude deviations spanning from −40% to −20%; when calibrating at 2 × 10−4 m/s, 84 MS showcase amplitude deviations in the range of −50% to 0. Similarly, at the calibration amplitude of 1 × 10−4 m/s, 86 MS exhibit amplitude deviations between −50% and 0. These results imply that as the calibration amplitude diminishes, the number of MS that satisfy the given condition gradually decreases. Moreover, each calibration amplitude has a limited number of MS with amplitude deviations significantly beyond the normal range, signifying improper functionality. It is evident that the test can differentiate MS with abnormal measurement amplitudes. We graphed the outcomes separately for each calibration amplitude for a more granular analysis of the amplitude test results, as illustrated in Amplitude deviation analysis enables the assessment of accuracy in measuring different amplitudes using MS, while amplitude linearity analysis evaluates the consistency of measurements across varying amplitudes. The data source for amplitude linearity is identical to that of amplitude deviation. The amplitude linearity is calculated as demonstrated in Equation (3). As depicted in The previous elucidates the equipment and procedure employed for calibrating MS. However, it is of paramount importance to establish criteria that facilitate the determination of the normal operational status of old MS. Twenty new MS were assessed using CS18VLF, and their amplitude–frequency calibration indices were computed employing Equations (1)–(3). Subsequently, the evaluative critical value was determined by comparing the discrepancies between the calibration indices of the new and old MS. The calibration method and process remain consistent with the previous description and will not be reiterated here. −4 m/s to 30% at 1 × 10−4 m/s. This pattern can be attributed to the reduction in the denominator in Equation (2). Similarly, the amplitude deviation of the old MS has been adjusted by excluding the outliers identified in −4 m/s to −250% at 1 × 10−4 m/s. Subsequent data analysis revealed that the majority of the old MS had an absolute amplitude deviation of less than 55%. Therefore, the established criterion for determining the normality of MS amplitude measurement is an absolute amplitude deviation below 55%. Upon evaluating the old MS against this criterion, it was found that 96 of them fulfilled the specified requirements. Microseismic source localization is a fundamental task within MMS. Various methods have been proposed, including the P-wave initial arrival method, the P-wave and S-wave time difference method, and the azimuth angle method (for three-component MS), to accurately determine the microseismic source position ,29,30,31Given the complexity of actual vibration waveforms, a simplification was made using a triangular waveform  to establish a mathematical relationship. This relationship elucidates the influence of the parameter Equation (5) uses assumed values for Various methods exist for calculating the energy of a microseismic event, including the Gutenberg–Richter, energy density, duration of vibration, diagram integral, and discrete methods . In thisIn alignment with our prior discussions, we have chosen to simplify when analyzing the impact of amplitude bias on energy calculation results. Specifically, we assume a localization scenario that utilizes one sensor with amplitude error alongside three sensors that are assumed to be error-free. At the same time, the energy we discuss is the energy calculated by the sensor with amplitude error. A positive amplitude deviation results in an advance in the initial arrival time of the P wave, consequently advancing the onset time of the MS source. In such cases, this corresponds to a reduction in Currently, numerous researchers have conducted extensive research in data mining, microseismic signal classification, and related areas based on microseismic monitoring databases . However(1)The comparison test between old and new MS using CS18VLF reveals that MS can experience failure after prolonged use in underground conditions. This failure is primarily characterized by significant deviations from the normal range in the measured frequency and amplitude. The methodology employed in this study has demonstrated its efficacy in identifying these faulty sensors.(2)Using a comparative analysis of frequency deviation, amplitude deviation, and amplitude linearity between the old and new MS, the critical threshold for evaluating the effectiveness of MS was established. A sensor is deemed functional when its absolute frequency deviation is below 5%, absolute amplitude deviation is below 55%, and amplitude linearity exceeds 0.95.(3)Under normal operating, the frequency deviation of the MS is significantly smaller than the amplitude deviation. Consequently, the effect of frequency deviation on microseismic energy can be deemed negligible.(4)Simplified waveform analysis reveals a linear relationship between amplitude deviation and microseismic localization results. However, microseismic energy is influenced by both amplitude deviation and localization results. Therefore, pinpointing the precise impact of amplitude deviations on microseismic energy is challenging.This study effectively addresses the research gap in the calibration of microseismic sensors and emphasizes the crucial importance of this calibration. The research systematically investigates and examines the microseismic monitoring system used in Chinese coal mines, thoroughly explaining the principles governing the monitoring and calibration of MS. The CS18VLF was constructed to promote research in this area, enabling a range of meticulous calibration experiments for MS. The key findings from this research can be summarized as follows:


This study involves an investigation of the effects of various cooking temperatures, freeze–thaw processes, and food preservatives on the quality and shelf-life of sous vide Mediterranean mussels. Cooking temperatures of 80 °C or above significantly improved the microbiological quality, with bacterial counts remaining within the acceptability range for human consumption even after 21 days of refrigerated storage. Fast freezing followed by slow thawing preserved the highest moisture content, potentially improving texture. Sensory analysis revealed that refrigerated sous vide mussels maintained a comparable taste to freshly cooked samples. Frozen samples reheated via microwaving exhibited more intense flavour than pan-reheated or fresh mussels. Food additives, including citric acid, potassium benzoate, and potassium sorbate, alone or in combination with grape seed oil, significantly reduced total volatile basic nitrogen and thiobarbituric acid-reactive substances during 28 days of storage, indicating decreased spoilage and lipid oxidation. Mussels with a combination of these additives registered a nitrogen content as low as 22 mg of N/100g after 28 days, well below the limit of acceptability (<35 mg of N/100g). Food additives also inhibited bacterial growth, with mesophilic bacteria count below 3.35 Log CFU/g after 28 days, compared with 5.37 Log CFU/g in control samples. This study provides valuable insights for developing optimal cooking and preservation methods for sous vide cooked seafood, underscoring the need for further research on optimal cooking and freeze–thaw protocols for various seafood types. Mytilus galloprovincialis are important edible bivalve mussels, widely distributed and farmed in the Mediterranean area. They represent an important economic income source in Europe. Spain is the largest mussel-producing country in Europe, with an annual production of more than 200 KT [n 200 KT , and ItaFresh mussels are highly perishable, even under refrigerated conditions. This perishability limits their shelf life and poses a challenge to meet the market demand, especially in regions where these mussels are not locally sourced. The transformation processes must take into account two essential factors: to increase the shelf life of the product and to have a product with acceptable taste and flavour. Mussel processing enables the commercialisation of products for new consumers and new markets while extending their limited shelf life . HoweverIn recent years, there has been a growing international interest in the sous vide preparation of seafood . The souRecently, Bongiorno et al. (2018) evaluated the chemical, microbiological, and sensory quality during chilled storage (+3 °C) of sous vide mussels. However, the physical properties and the opportunity to freeze the vacuum-packed product were not evaluated in this study. Moreover, the sensory differences between various reheating methods have never been studied for vacuum-packed mussels before. Indeed, several studies, suggested that the sensory characteristics of precooked fish products could be affected by the type of reheating process ,17.Salmonella spp., Listeria monocytogenes, and Clostridium botulinum. These pathogens can be effectively removed via heat treatments with a proper range of time/temperature, as stated by the European Food Safety Authority (EFSA) [The main issue concerning the development of new seafood products is the risk of pathogenic microorganisms such as hepatitis A virus, y (EFSA) .From this perspective, this study aims to pioneer novel methodologies for creating sous vide mussels that ensure microbiological safety and prolonged high-quality organoleptic characteristics. Specifically, the research objective is the evaluation of total volatile basic nitrogen (TVB-N), thiobarbituric acid reactive substances (TBARSs), and physical attributes, as they serve as critical indicators of product quality and freshness. Furthermore, this study involves an in-depth assessment of the microbiological quality of sous vide mussels under varying cold storage conditions. The overarching goal is to quantify the correlation between these analytical metrics and the sensory properties of mussels. By creating an integrative framework of these variables, the study seeks to formulate best practices for the sous vide preparation and storage of mussels that offer high quality and safety standards.Mytilus galloprovincialis) farmed in Campania Region were purchased at a fish market in Naples . The mussels were transported in the laboratory in about thirty minutes using a refrigerated polystyrene box and placed in a refrigerated chamber (+4 °C) before being processed. They were manually sorted to eliminate dead or damaged molluscs and subjected to cleaning and shell washing. The mussels were then classified based on weight and size. For the experiments, only mussels with a length of more than 6 cm and an average weight of 22 ± 5 g were used.Live refrigerated mussels  on 30 mussels of each batch and was defined as the percent ratio between cooked meat weight and the sum of cooked meat weight and shell weight. The meat yield (MY) was calculated as the percent ratio of wet meat weight to total live weight . Only muMussel samples of 500 ± 10 g were vacuum-sealed in 20 × 30 cm PA/PP sous vide cooking bags  designed for vacuum storage and sous vide cooking up to 121 °C for 60 min. The mussels were then cooked submerged in a thermostatic bath , Italy) at four different times/temperatures: 72 °C for 450 s, 80 °C for 270 s, 90 °C for 90 s, and 100 °C for 40 s. The cooking curves were chosen on the basis of the recommendations of the EFSA panel on biological hazards regarding the thermal treatment that could be applied to bivalve molluscs to eliminate pathogenic microorganisms such as hepatitis A [The attainment of the target internal temperature was ascertained using a thermocouple probe, which was carefully inserted into the centre of a mussel through a small perforation made in the shell. To promptly halt the cooking process, the samples were swiftly removed and placed in a blast chiller set to a temperature of −1 °C for a duration of 15 min. Once cooled, the samples were stored in a refrigerated environment maintained at a constant temperature of +4 °C. Analytical samples were systematically taken at predetermined time intervals, specifically at the commencement of the storage period and subsequently on the 7th, 14th, 21st, and 28th days. The best procedure of sous vide cooking in terms of microbial and mechanical quality was also used to prepare a frozen product. Cooked mussels were subjected to three different freezing curves from +4 °C to −20 °C: 30 min ; 45 min ; 60 min . The samples for analysis were taken after 14 d from storage at −18 ± 1 °C. Two thawing methods were assessed: fast thawing (FT) in a microwave  at 750 W for 180 s and slow thawing at +4 °C for 16 h. Reheating for both treatments was performed until reaching a mean internal mussel temperature of +65 ± 3 °C.After the preliminary trials, to increase the shelf-life of the refrigerated product, the optimal sous vide cooking procedure was used to test different preservative treatments using citric acid, grape seed extract, potassium benzoate, and potassium sorbate. These additives were added in a liquid form from stock solutions, and aliquots were pipetted into the bags with mussels and shaken before cooking. The following formulations were used in the samples: (a) pH of up to 4.6 by adding citric acid (1 g/kg) in the vacuum bag before the cooking process (AC); (b) pH of up to 4.6 by adding citric acid and 20 g/kg of grape seed oil as antioxidant (BC); (c) pH of up to 4.6 by adding citric acid and 1 g/kg of potassium benzoate as antimicrobial agent, and 1 g/kg of potassium ascorbate as antioxidant (CM). The selected concentration of additives was chosen after preliminary trials on microbial and chemical quality over time. The quantities of the selected preservatives were all below the legal limits imposed by the European community for each compound.TVB-N, TBARS, and microbiological analyses were performed after 0, 7, 14, 21, and 28 days of refrigerated storage on the preservative treatments and assessed against a control group (control) without any compound added but applying the same cooking and storing conditions.The shear force of mussel meat was determined using a texture analyser equipped with a 6 cm large blade  and a load cell of 50 N applying a 0.05 N preload. The test was conducted on 15 single-shelled mussels for each treatment. The data were calculated and analysed with Texture Lab Pro Software (version 1.1).The colour analysis, as a quality indicator of mussels, was studied using an IRIS high-resolution imaging visual analyser . Colour values were expressed as L*a*b* values according to the method of Jeon et al. (2020) on 15 mussels for each treatment .Finally, the moisture of cooked mussels, which is relevant for the assessment of juiciness, was gravimetrically assessed by drying 10 shelled and drained mussels from each treatment in an oven at 105 °C for 16 h.TVB-N content was determined according to the method described by Sadok et al. (1996)  on 5 musThe determination of 2-thiobarbituric acid was based on the spectrophotometric quantitation of the pink complex formed after the reaction of one molecule of malondialdehyde (MDA) with two molecules of 2-thiobarbituric acid (TBA), according to the method of Goulas and Kontominas (2005). TBARS values were expressed in units of mg/malonaldehyde/kg sample.L. monocytogenes (ISO 11290-1:2017) and Salmonella spp. (ISO 6579-1:2017).Microbiological analyses were performed on refrigerated products 0, 7, 14, 21, and 28 days after cooking. Analyses were carried out to evaluate the presence/absence of the following pathogenic microorganisms: For all the tests, 25 g of each whole sample was diluted 1:10 in saline–peptone water sterile bags and homogenised using a stomacher.Enterobacteriaceae was performed according to standard UNI-EN-ISO 21528-2:2017 [E. coli was determined according to the ISO standard 16649-3:2015. Total mesophilic and psychotropic bacteria were determined according to the ISO standard UNI EN ISO 4833-1:2022 using Plate Count Agar . Coagulase-positive staphylococci were determined according to UNI ISO 6888-1:2018 using Baird Parker Agar . Mesophilic lactobacillus (LAB) was determined according to the ISO standard 15214:2015 using de Man, Rogosa, and Sharpe Medium  after incubation at 25 °C for 2 days. Pseudomonas was enumerated on Cetrimide Fusidin Cephaloridine Agar  and yeast and mould on Yeast Extract Glucose Chloramphenicol Agar . Microbiological data were transformed into logarithms of the number of colony-forming units (CFU/g). All plates were examined visually for typical colony types and morphology characteristics associated with each growth medium. The analyses were performed in triplicate agar plates on serial decimal dilutions of each mussel homogenate.The determination of 8-2:2017  using ViThe sensory analyses were performed according to the method proposed by Turan et al., (2007) , with miTwo different sessions were performed to evaluate the chilled stored sous vide mussels and frozen mussels. The chilled stored product was evaluated after two reheating treatments: in a pan with a lid for 2 min at medium-high flame and by placing it directly in a vacuum bag in a microwave at 650 W for 1 min. The frozen product was evaluated for two reheating processes: in a pan with a lid for 3 min at medium-high flame and by placing it directly in a vacuum bag in a microwave at 650 W for 2 min. These reheating methods were chosen as they are the most common homemade methods for these kinds of products . Both reIn both panel trials, a comparison with live fresh mussels was also performed in order to observe the sensory differences between sous vide mussels and fresh products. The fresh mussels were also cooked regularly on a pan with a lid until all the shells were open.p < 0.05). Tukey’s test was used for post hoc analysis when there were significant differences among the samples. In cases in which samples were compared in pairs, a two-sample t-test was used. Data were analysed using IBM© SPSS© Statistics software Ver. 23 .All treatments were carried out at least in triplicate, and for the analyses, average values with standard deviations were reported. One-way ANOVA was carried out using raw data to test for significant differences among the samples . The lightness parameters remained stable over time, except for the samples treated at 90 and 100 °C in which there was a marked decrease in this parameter. For the red/green value instead (a*), the samples cooked at 72 °C showed significant differences from those cooked at 90 and 100 °C. From a colorimetric point of view, the samples cooked at 72 and 80 °C were those that exhibited greater stability than the other samples with higher temperatures. Colour changes in shellfish are often a result of mixed complex reactions such as protein denaturation, lipid oxidation, and Maillard reaction [In terms of lightness (L*), yellowness (b*), and redness (a*), some significant differences were observed between the samples cooked at various treatment temperatures , but it significantly affected this parameter after 21 days of storage. This result is in accordance with the moisture trends shown in The term “shear force” in food technology is defined as any cutting action that splits a product into two fragments. In the case of meat and fish, the most important technological parameter is the one that influences the structure and conformation of myofibrillar and connective tissue proteins, i.e., the temperature of cooking . In factThe effects of different combinations of freezing and thawing on the physical properties of sous vide cooked mussels are reported in The L value represents lightness, with higher values indicating lighter colours. Fast freezing followed by slow thawing (FF-ST) and medium freezing followed by slow thawing (MF-ST) resulted in the lightest colour , while slow freezing followed by slow thawing (SF-ST) resulted in the darkest colour (67.0). The a value represents the green-red colour spectrum. More negative values indicate a shift towards green, while more positive values indicate a shift towards red. The mussels subjected to slow freezing, followed by either fast or slow thawing (SF-FT and SF-ST), showed a stronger shift towards green  than the mussels subjected to medium freezing followed by slow thawing . The b value represents the blue-yellow colour spectrum. More positive values indicate a shift towards yellow. Medium freezing followed by fast thawing (MF-FT) resulted in the most yellow colour (41.7), while slow freezing followed by slow thawing (SF-ST) resulted in the least yellow colour (37.5). As reported by Sun et al., 2021, slow thawing also worsened the b* value of carp, which was mainly caused in response to the loss of carp juice and oxidation of the pigmented proteins . In factThe moisture content was highest in mussels subjected to fast freezing followed by slow thawing  and lowest in mussels subjected to slow freezing followed by slow thawing . Moisture content varied significantly, with the highest in mussels undergoing fast freezing followed by slow thawing (FF-ST), suggesting that this combination might better preserve the water content in mussels, potentially leading to a juicier texture.Shear force was highest in mussels subjected to fast freezing followed by slow thawing  and medium freezing followed by slow thawing . The lowest shear force was observed in mussels subjected to medium freezing followed by fast thawing .It is reported that the formation of ice crystals physically destroys the cell structure during the frozen storage of mussels, resulting in the release of cytoplasm after thawing. Consequently, frozen storage decreases the product texture . MoreoveWith these assumptions, the mussels treated with FF-FT were selected for subsequent sensorial analysis in order to test the mussels with less damage due to freezing.TVB-N is widely used to determine the freshness of seafood products in the industry. TVB-N content of sous vide cooked mussels is reported in The initial TVB-N content of samples is an indicative value of freshness for raw fish materials . Howeverp > 0.05). For all treatments, there was a gradual increase in TVB-N from 0 to 14 days, reaching values higher than 30 mg N/100 g after 21 days of storage. This is in contrast to the work of Bongiorno et al., which stated that after 50 days of refrigerated storage, the TVB-N values of vacuum-packed mussels remained constant. However, the authors added brine solutions and stored the mussels at +3 °C, which could affect the TVB-N content during storage. In our case, the mussels were packed without any brine solution or preservatives. Nevertheless, our data are in agreement with [In our case, at the initial storage time, the TVB-N value ranged between 6 and 8 mg N/100 g, which is in agreement with , who repent with , which rent with .The results of the TBARS analysis are reported in 2 dissolved in the tissue, could also favour the self-oxidation of PUFA.The increase in TBARS values in mussel meat during cold storage indicates the decomposition of hydroperoxides into the secondary oxidation products during lipid oxidation . The lipThe changes in microbiological quality during the storage period are reported in The highest total mesophilic bacteria (TMB) content was observed for samples cooked at 72 °C, with a total bacteria content of 1.21 Log CFU/g after 7 days of refrigerated storage (+4 °C), and reaching a value of 5.73 after 21 days. At 80, 90, and 100 °C cooking temperatures, mesophilic bacteria were detectable after 14 d of storage, with an exponential increase up to 21 days.Pseudomonas sp. and LAB, which were absent in all other samples. The LAB count was determined because LAB has been reported to be the dominant spoilage flora in vacuum-packaged mussels [The ICMSF (1986) recommends the value of 7 Log CFU/g as the upper acceptability limit of the total viable count (TVC) for freshwater fish and seafood . For coo mussels . In our  mussels .Pseudomonas sp., yeast, and moulds were inhibited when the samples were cooked sous vide at a temperature higher than 80 °C. This was also in agreement with the results of Rhodehamel (1992), which stated that Pseudomonas growth and yeast growth are prevented when cooking under sous vide conditions [From a microbiological perspective, our study revealed that the samples cooked at temperatures higher than 80 °C did not show notable differences, thus suggesting that cooking at 80 °C was sufficient to stabilise the product. Our results are in line with those from Bongiorno et al. (2018), in particular for the spoilage microorganism count. In fact, in our study, nditions .Listeria monocytogenes, and Salmonella were absent in all the mussel samples analysed.Additionally, in our study, we ascertained that potentially harmful pathogens such as anaerobic sulphite-reducing clostridia, An important contribution of our study is the sensory analysis of sous vide mussels under varying cold storage conditions. The cooked and refrigerated samples did not show any significant difference from the fresh mussels cooked using the traditional method (control). Under these conditions, all the samples reported a high succulence, with a low level of graininess. These results are in line with those of Bongiorno et al. (2018), which reported no significant differences between the sous vide cook–chill mussels and conventionally cooked mussels after 14 days of storage (3 °C) . In the The results of the optimisation of food products by means of food additives are shown in With the addition of preservatives, a significant decrease in TVB-N was observed on the sous vide cooked mussel samples during 28 days of storage. In p < 0.05). No significant differences were observed between samples during the first 7 days of storage. However, from day 14, increasingly marked differences were observed between the preserved samples and the control without additives. In fact, after 28 days of refrigerated storage, the MDA content of mussels in the control was almost 1.8 mg MDA/Kg, while the mussels stored with additives registered values 3 times lower (in the case of CM and BC). It is reported in the literature that citric acid is able to inhibit the rancidity development of Wels catfish fillets [Pseudomonas lactis bacteria [Concerning the TBARS experiments, our results showed that the use of all three preservative treatments led to a significant reduction in the rancidity of refrigerated sous vide mussels during 28 days of storage  were found to maintain colour stability, an important factor for product acceptance. In the context of freezing and thawing processes, our findings indicated that the employed methods could substantially impact the mussels’ physical properties. Specifically, fast freezing followed by slow thawing (FF-ST) yielded the highest moisture content, highlighting a potential strategy for better preserving the moisture in mussels and possibly improving their texture.The investigation of food preservatives revealed their potential in extending the shelf life of sous vide cooked mussels. Citric acid was found to be the most effective preservative in reducing microbial growth, maintaining product quality, and extending shelf life compared with other preservatives. However, the preservative choice should also consider their potential effects on taste, texture, and overall consumer acceptability.These findings underscore the need for further research on optimal cooking and freeze–thaw protocols for various seafood types, and a more detailed exploration of the synergistic effects of preservatives, cooking, and freeze–thaw processes. Overall, this study provides a scientific foundation for the seafood industry to develop best practices for the sous vide cooking and preservation of mussels.