In the present study, we aim to compare the performance of Q.Clear  and OSEM  for 68Ga-DOTATATE (68Ga-DOTA) PET studies, both visually and quantitatively.Image quality and quantitative accuracy of positron emission tomography (PET) depend on several factors such as uptake time, scanner characteristics and image reconstruction methods. Ordered subset expectation maximization (OSEM) is considered the gold standard for image reconstruction. Penalized-likelihood estimation (PL) algorithms have been recently developed for PET reconstruction to improve quantitation accuracy while maintaining or even improving image quality. In PL algorithms, a regularization parameter 68Ga-DOTA studies were included. The data were acquired in list mode and were reconstructed using 3D OSEM and Q.Clear with various values of β and various acquisition times per bed position (bp), thus generating images with reduced injected dose . An additional analysis adding β values up to 1500, 1700 and 3000 for 1.5, 1.0 and 0.5 min/bp, respectively, was performed for a random sample of 8 studies. Evaluation was performed using a phantom and clinical data. Two experienced nuclear medicine physicians blinded to the variables assessed the image quality visually.Thirty consecutive whole-body β = 1100, 1300 and 3000, respectively, resulted in images with noise equivalence to 3D OSEM (1.5 min/bp) with a mean increase in SUVmax of 14%, 13% and 4%, an increase in SNR of 30%, 24% and 10%, and an increase in SBR of 13%, 13% and 2%. Visual assessment yielded similar results for β values of 1100–1400 and 1300–1600 for 1.5 and 1.0 min/bp, respectively, although for 0.5 min/bp there was no significant improvement compared to OSEM.Clinical images reconstructed with Q.Clear, set at 1.5, 1.0 and 0.5 min/bp using 68Ga-DOTA reconstructions with Q.Clear, 1.5 and 1.0 min/bp, resulted in increased tumor SUVmax and in improved SNR and SBR at a similar level of noise compared to 3D OSEM. Q.Clear with β = 1300–1600 enables one-third reduction of acquisition time or injected dose, with similar image quality compared to 3D OSEM. The current procedure guidelines for PET/CT tumor imaging with 68Ga-DOTA-conjugated peptides [68Ga-DOTA availability is limited by 68Ge/68Ga generator capacity, reduced injected doses with preserved image quality should be investigated, with the ultimate aim of defining the most appropriately low level of injected activities.Positron emission tomography (PET) computed tomography (CT) with peptides  recommenImage quality and quantitative accuracy of PET studies are highly influenced by several factors such as injected activity, uptake time, scanner characteristics and image reconstruction methods. Currently, statistical iterative reconstruction methods are the most widely used image reconstruction methods  and the β parameter which controls the penalization of relative differences between neighboring pixels [β-positive regularization parameter controls the trade-off between noise level and resolution, as opposed to several iterations, subsets and post-filter with OSEM. Moreover, with PL algorithms, excessive smoothing over large edges and Gibbs artifacts from PSF modeling are avoided [Penalized-likelihood estimation (PL) reconstruction algorithms have been recently developed and clinically implemented to improve quantitation accuracy, while maintaining or even improving image quality. PL algorithms allow for fully convergent iterative reconstruction, leading to higher image contrast than for OSEM while limiting noise . Insteadg pixels . Therefo avoided .β factor but its value depends on different parameters such as the radiopharmaceutical, the injected activity, the acquisition time per bed position (bp), the PET scanner and the image reconstruction algorithm. In both phantom and clinical 18F-FDG studies, Q.Clear has been shown to provide better quantitation accuracy and image quality than OSEM [18F-FDG examinations, the optimal β ranges between 500 and 600 when using a (administered activity (MBq/kg) × acquisition time (min/bp)) product of 6. A recent study [β factor between 300 and 450, Q.Clear is superior to OSEM  including time-of-flight (TOF) information and PSF modeling, in terms of CR and SNR. For 68Ga-DOTA, one preliminary study by Lantos et al. [β factor between 350 and 450 in clinical practice for all the studied radiopharmaceuticals while the study conducted by ter Voert et al. [68Ga-PSMA, a β value between 400 and 550 could be optimal. A recent study conducted by Lindström et al. [68Ga-DOTATOC studies concluded that β values equal to or higher than 400 result in noise levels equal to or lower than those of OSEM  with improved SNR and SBR.Q.Clear is the commercially available version of the PL algorithm introduced by General Electric (GE) Healthcare. There is no one optimal penalization han OSEM –10. A sthan OSEM  showed tnt study  showed ts et al.  in 10 pat et al.  concludem et al.  on thirt68Ga-DOTA examinations. We defined an optimal β value for the standard acquisition time, and we investigated optimal β values leading to similar or even superior image quality while the acquisition time or injected activity had been reduced. To achieve this goal, Q.Clear and OSEM acquisitions were compared by quantitative evaluation of phantom acquisitions and clinical studies, as well as by qualitative assessments.In the present study, we aimed to evaluate quantitatively and qualitatively the performance of Q.Clear with full and reduced acquisition time or injected activity, compared to our local optimized OSEM + TOF + PSF  and to the manufacturer’s recommendation OSEM + TOF + PSF  with or without time or dose reduction for whole-body 68Ga-DOTA PET/CT scans at our institution. Inclusion criteria for this study were (a) images were acquired on General Electric (GE) Healthcare Discovery MI PET/CT scanner  and (b) at least one focus of pathological 68Ga-DOTA uptake was noted on the PET/CT study.Between March 17, 2019, and June 19, 2019, 65 consecutive patients underwent 68Ga-DOTA PET/CT on another PET/CT system  at our institution. The remaining 30 patients  were included in this single-center retrospective study.Of the 65 patients, 35 patients were excluded from the study, 21 patients with normal studies without focus of pathological uptake and 14 patients performed All studies were performed on a Discovery MI PET/CT . The system combines a 128-slice computed tomography (CT) system and a 4-ring PET system with LightBurst digital detectors providing a 20-cm axial field-of-view and a 70-cm transaxial field-of-view. The system is TOF-capable with a timing resolution of 377 ps .68Ga), and lung insert provided with the phantom was filled with low-density material (polystyrene) and water. The phantom was filled to reach a target-to-background ratio of 4:1. The background region and spheres contained a 68Ga activity concentration of 2.48 kBq/mL and 9.92 kBq/mL, respectively, at the time of acquisition. The phantom images were acquired in list mode with an acquisition time of 3.0 min/bp.The National Electrical Manufacturers Association (NEMA) IEC image quality body phantom (IQBP) (Model PET/IEC-BODY/P)  was used68Ga-DOTA was injected intravenously following an administration protocol of 2 MBq/kg (minimum–maximum activity: 100–250 Mbq). The mean administered activity was 160.3 ± 32.0 MBq . The PET acquisition started at a mean of 68 ± 10 min  after tracer injection. All PET studies were performed from the proximal femur to the base of the skull (six–eight bed positions) and were acquired in list-mode with an acquisition time of 1.5 min/bp. Patients’ characteristics, injected dose and uptake time are summarized in Table 68Ga-DOTA PET images done by experienced physicians. The corresponding reconstructed images are defined as Hadassah OSEM reconstruction thereafter.Phantom and clinical images were first reconstructed with 1.5 min/bp and using the GE VUE Point FX-S algorithm (VPFX-S), a 3D maximum likelihood ordered subset expectation maximization (3D OSEM) image reconstruction algorithm using TOF information and PSF modeling with 3 iterations, 8 subsets and 6-mm post-processing filter. These settings have been adjusted at the installation of the system by the local manufacturer field engineer according to the visual evaluation of β and with 1.5, 1.0 and 0.5 min/bp acquisitions. The 1.5, 1.0 and 0.5 min/bp acquisitions were used to simulate standard, two-thirds and one-third acquisitions (time or injected dose). Images were reconstructed in a first time with β = 300, 400, 500, 600, 700, 800, 1000 and 1100 for the 1.5-min/bp acquisition, β = 600, 700, 800, 1000, 1100, 1200 and 1300 for the 1.0 min/bp acquisition and β = 800, 1000, 1200, 1300, 1400, 1500, 1600, 1800, 2000 and 2200 for the 0.5 min/bp acquisition. These values were chosen following an initial subjective visual assessment of clinical images performed by one of the authors .In addition, data were reconstructed using the Q.Clear algorithm with different values of the penalization factor β values are to be included in the analysis. Thus, an additional analysis adding β values of 1200, 1300, 1400, 1500 for 1.5 min/bp, 1400, 1500, 1600, 1700 for 1.0 min/bp and 2400, 2600, 2800, 3000 for 0.5 min/bp was performed for a random group of 8 patients. OSEM reconstruction recommended by the manufacturer to be used in clinical setting  [After a first quantitative analysis and visual assessment of the images reconstructed with the previous parameters, it seemed that higher  filter) , 19 was All data were corrected for scatter, random events, dead time and attenuation (using CT).Images were analyzed as detailed below and previously proposed by Lindström et al. .2) located away from the axial plane containing the sphere centers, divided by the mean activity concentration in these background ROIs. CNR was calculated as contrast recovery (CR) divided by BV as follows:Background variability (BV) and contrast-to-noise ratio (CNR) were calculated and compared. BV was defined as the SD of the activity concentration in large ROIs .with std of a large spherical VOI in normal liver normalized to SUVmean of the same VOI. SNR was calculated as lesion SUVmax divided by noise level. SBR was defined as lesion SUVmax divided by SUVmean of the normal liver VOI. For this analysis, up to three lesions per study  were delineated on the AW Workstation using a 41% SUVmax threshold. Lesion VOIs were first built on the Hadassah OSEM images, and bookmarks containing the location of these lesions were used to propagate and build new VOIs on reconstructed images with 41% thresholding. Also, lesions SUVmax values obtained for Hadassah OSEM and the reconstructed algorithms leading to similar level of noise were also analyzed and compared. The correlation of the SNR and SBR with the lesion size, penalization factor value, injected activity and patient weight has been evaluated for the three acquisition times.Level of noise, signal-to-noise ratio (SNR) and signal-to-background ratio (SBR) were calculated and compared. Level of noise was defined as SUVIn the first analysis, whether for the standard acquisition or for each of the simulated reduced injected activity studies, the five Q.Clear reconstructions leading to the best results in phantom and clinical images evaluations were visually compared with the Hadassah OSEM reconstruction by two blinded experienced nuclear medicine physicians (EG and SBH). A total of 90 image sets were assessed; every set consisted of 5 different reconstructions for 1.5, 1.0 and 0.5 min/bp, for each of the 30 patients included in the study. For the additional analysis, the four additional Q.Clear reconstructions and the manufacturer reference GE OSEM, were visually compared with the Hadassah OSEM reconstruction by an experienced reader (SBH). All data were anonymized regarding the reconstruction method, and numbers were randomly assigned. PET datasets were rated on a 4-point scale  for contrast, sharpness, noise, liver homogeneity, tumor detectability and overall image quality.p < 0.05), it was followed by post-hoc pairwise comparisons  according to Conover [p values lower than 0.05 were considered statistically significant. For the quantitative analysis, algorithms with significant Bonferroni corrected p values for SNR, SBR and noise were considered to outperform Hadassah OSEM. For the visual analysis, algorithms leading to significant Bonferroni corrected p values for the mean of all the rated parameters were considered superior to Hadassah OSEM.The mean values for each of the rated parameters by the two readers and the mean values of all scores were summarized. A non-parametric test, Friedman test, for multiple comparisons was performed to evaluate the differences between the various image reconstruction algorithms. When necessary, a correction for ties was applied . When Fr Conover , with Boβ and acquisition times of 1.5, 1.0 and 0.5 min/bp are shown in Fig. β ≥ 1000 for 1.5 min/bp, β ≥ 1400 for 1.0 min/bp and β ≥ 2200 for 0.5 min/bp allowed for similar or improved BV values compared to the Hadassah reconstruction method. Similarly, CNR values obtained with Q.Clear are higher than those with Hadassah OSEM when using β ≥ 1000 for 1.5 min/bp and β ≥ 1300–1500 for 1.0 min/bp. For 0.5 min/bp, improved CNR results are reached for β ≥ 2200 for the large 28- and 37-mm spheres while for the 17- and 13-mm spheres CNR values were about 25% lower compared to the Hadassah OSEM reconstruction method. Of note, for all reconstructions, the standard Hadassah OSEM reconstruction led to better CNR for the smallest 10-mm diameter sphere.Background variability and contrast-to-noise ratio obtained from images reconstructed with Hadassah OSEM, the manufacturer (GE OSEM) recommendation  and Q.Clear with different values of the penalization factor β ≥ 400 for 1.5 min/bp and of β ≥ 600 allowed to obtain a better BV and CNR. For 0.5 min/bp, Q.Clear reconstructions with β values higher than 1000 outperforms the BV obtained when using the manufacturer recommendation and β ≥ 800 allowed to obtain higher or similar CNR values for all the spheres except for the two smallest ones.Comparing Q.Clear reconstructions to the reconstruction recommended by the manufacturer (GE OSEM), penalization factors of β and different acquisition times per bed position . The images demonstrate better BV and CR with increasing β factor for all the acquisition times.Figure p values for a given algorithm compared to Hadassah OSEM are also shown in Fig. Y-axis). A total of 75 lesions for the first analysis and 19 for the additional one per reconstruction were used for comparative analysis of the SNR and SBR obtained using Q.Clear, with different β values and acquisition times, to those obtained with the Hadassah OSEM reconstruction and 1.5 min/bp. As described above, a single large normal liver VOI was used in each study for noise level comparison.Figure β values. For the smallest β values used in this study , the noise level increased by about 85%, 50% and 65% on average, respectively, compared to the standard Hadassah OSEM reconstruction method. However, the noise level was lower than the standard by 15% using β = 1500 with 1.5 min/bp and 10% using β = 1700 with 1.0 min/bp or β = 3000 with 0.5 min/bp. For the latter, the SNR increased by 30%, 24% and 12%, respectively. The SBR was also higher than the Hadassah standard with a mean increase of 13% for 1.5 and 1.0 min/bp and lower by 2% in average for 0.5 min/bp. The SUVmax increased by 14%, 13% and 4% on average, respectively. The statistical results show that a β = 1100–1400 for 1.5 min/bp outperforms Hadassah OSEM in terms of SNR, SBR and noise level with Bonferroni corrected p < 0.05   with an average improvement of 30%, 28% and 20%, respectively. For 1.0 min/bp, Q.Clear with β = 700–800 is also better than GE OSEM with SNR and SBR increasing, respectively, by 27% and 14% and noise level decreasing by 25% on average. The reduction of the acquisition time to 0.5 min/bp with Q.Clear allowed to obtain improved SNR, SBR and noise levels but improvements are not significant. Compared to Hadassah OSEM, GE OSEM has a lower SNR by 25%, a higher noise level by 60% and an improved SBR by 18%.Comparison of GE OSEM and Q.Clear reconstructions shows that for 1.5 min/bp a 3 showing the highest improvement in both SNR and SBR  or uptake time. The improvement in SNR or SBR was stable despite variation of these parameters. However, there was a correlation with the lesion volume, foci with volumes lower than 1–2 cmβ = 600–1100 for 1.5 min/bp, β = 800–1300 for 1.0 min/bp, β = 1500–2200 for 0.5 min/bp) were compared with the Hadassah OSEM reconstruction at first stage  and GE OSEM were then added to the analysis , respectively, compared to 3.38 for the standard. Similarly, 1.0-min/bp images reconstructed with Q.Clear and β = 1200 and 1300 scored better than Hadassah OSEM with 1.5 min/bp with Bonferroni corrected p values of 1∙10−5 and 4∙10−6, respectively. However, for 0.5 min/bp, Q.Clear scored 3.60 for β = 2200 compared to 3.61 for Hadassah with 1.5 min/bp (corrected p = 0.26).In the first analysis, for the full-time acquisition of 1.5 min/bp, Q.Clear reconstruction with 0−7 Fig. . Reconstβ values and added GE OSEM for comparison in 8 studies. It is noteworthy that higher β values improved all the variables except for tumor detectability. Reconstructions with β = 1500 and 1700 for 1.5 and 1.0 min/bp ranked better than Hadassah OSEM with mean scores of 3.64 and 3.54 compared to 3.21 and 3.07, respectively . The reconstruction recommended by the manufacturer, GE OSEM, ranked below Hadassah OSEM for all the aspects except contrast and sharpness. Comparing GE OSEM to Q.Clear, β of 1200 and 1400 for 1.5 and 1.0 min/bp, respectively, allowed to obtain higher mean grades with Bonferroni corrected p < 0.05 .Our standard Hadassah OSEM reconstruction was also compared to the OSEM reconstruction recommended by the manufacturer, GE OSEM. From phantom and image analysis, it is clear that Hadassah OSEM shows better BV, CNR, SNR and noise level. The only point where GE OSEM is better is SBR. Similarly, the visual assessment ranked GE OSEM at a lower level than Hadassah OSEM for overall image quality, noise and tumor detectability. The higher level of noise in GE OSEM may not enable visualization of small lesions, leading consequently to reduced tumor detectability. These differences cause extremely different β for 68Ga tracers. In a preliminary study conducted by Lantos et al. [β value of 350–450 in clinical practice for all radiopharmaceuticals, including 68Ga-DOTA. The much higher β values obtained in our study can be due to different reasons. First of all, Lantos et al. compared the SUVmax of the smallest lesion of 10 patients using different reconstructions . As shown in the present study  with improved SNR and SBR. The OSEM reconstruction algorithm used in [β value around 500 is sufficient to outperform GE OSEM in terms of noise, SNR and SBR (Bonferroni corrected p < 0.05). Ter Voert et al. [68Ga-PSMA, focusing on 2-min emission data of the pelvic region. Longer acquisition times per bed position compared to our study (2–3 min/bp vs 1.5 min/bp in our study) may lead to lower optimal β values. In addition, differences in uptake and biodistribution between 68Ga-PSMA and 68Ga-DOTA tracers and focus on the pelvic region may affect the choice of the β factor. Finally, the OSEM reconstruction with 3 iterations, 28 subsets and 5-mm filter used in references [β values obtained there. Indeed, taking into account that the higher number of subsets used the higher the noise in the resulting image is [β values were sufficient to obtain a similar noise level and higher SUVmax compared to the OSEM.Previous studies also investigated optimal values of s et al. , the autthose of  is challthose of  showed t used in  correspot et al.  and Svirt et al.  proposedferences , 22 coulimage is , it woulβ values obtained in our study for 68Ga-DOTA examinations are higher than the values obtained by Lindström et al. [β of about 400 using an acquisition time of 3 min/bp. This result is in accordance with our observations when comparing GE OSEM to Q.Clear. Caribé et al. [β value to be used for an acquisition time of 1.07 min/bp. As shown above in the “Results” section, our results suggest that Q.Clear with β = 700–800 is significantly better than GE OSEM. This concords with Caribé et al.’s [Also, the optimal m et al.  and Carim et al.  for FDG m et al.  determiné et al.  investiget al.’s  results β and acquisition times for a total of 25 reconstructions per study or 750 reconstructions for the 30 included studies, each reconstruction taking up to 5 min. Therefore, the additional analysis has been performed on a random group of 8 studies. Assessment in a larger patient cohort would allow a more certain implementation in clinical practice. Finally, our study has been conducted on a specific PET/CT system, the Discovery MI. It would be interesting to investigate and confirm the β values found here on other systems of the same and different vendors with different PL algorithms.There are several limitations to our study. First, the physicians who assessed the images first read a training case in consensus in order to determine the scoring method. For overall image quality, the physicians chose to focus essentially on noise in the images. As a matter of fact, overall image quality and noise level obtained similar scores, regardless of the acquisition time Fig. . This ma68Ga-DOTA studies reconstructed with Q.Clear and adequate values of the penalization factor β resulted in increased tumor SUVmax and in improved SNR and SBR at a similar level of noise compared to our local optimized OSEM . The optimal β value for 1.5 min/bp was 1100–1400 and lead to better image quality than OSEM. Also, Q.Clear allowed to shorten the acquisition time by one-third, resulting in better image quality than OSEM (1.5 min/bp) with β = 1300–1600. For 0.5 min/bp, the β values assessed did not allow to obtain significantly improved results compared to OSEM. The results of the present study indicate that injected activities or acquisition time can be lowered by one-third for 68Ga-DOTA studies when reconstructing the data using the Q.Clear algorithm.


Among them, the generation of data sets was mainly due to multichannel data mining methods, such as high extracted ion chromatogram (HEIC), diagnostic product ion (DPI), and neutral loss filtering (NLF). A total of 60 metabolites (including the prototype compound) were identified in positive and negative ion modes based on intuitive and useful data such as the standard's cleavage rule, accurate molecular mass, and chromatographic retention time. The results showed that a series of biological reactions of phillyrin in vivo mainly included methylation, hydroxylation, hydrogenation, sulfonation, glucuronidation, demethylation, and dehydrogenation and their composite reactions. In summary, this study not only comprehensively explained the in vivo metabolism of phillyrin, but also proposed an effective strategy to quickly analyze and identify the metabolites of natural pharmaceutical ingredients in nature.Phillyrin, a well-known bisepoxylignan, has been shown to have many critical pharmacological activities. In this study, a novel strategy for the extensive acquisition and use of data was established based on UHPLC-Q-Exactive mass spectrometry to analyze and identify the  Forsythia suspensa (Thunb.) Vahl in plants belongs to the family Oleaceae , H [0–60], O [0–25], S [0–2], N [0–5], and ring double bond (RDB) equivalent value [0–15]. At the same time, MetWorks (Version 1.3) and Mass Frontier (Version 8.0) software  were used as auxiliary tools for mass fragmentation behaviors analysis and structural identification.A Thermo Xcalibur 2.1 workstation  was utilized to acquire and process HR-ESI-MSin vivo. First of all, the ESI-MSn data set of the samples in both positive and negative ion modes were obtained by using the data-dependent scanning (DDS) acquisition method. Secondly, the common metabolites of phillyrin were determined according to literature reports and HREIC search, thereby establishing the screening templates for phillyrin metabolites. Then, by analyzing the mass fragmentation behaviors of the reference standards, the reasonable DPIs and NLFs of phillyrin were summarized. Afterward, based on the chromatographic retention time, the established DPIs and NLFs, and the corresponding calculated ClogP values, the structure of a series of metabolites of phillyrin could be identified. Finally, the metabolic pathway of phillyrin can be inferred from the above metabolic data. The entire research process of this strategy is shown in This study constructed an effective new strategy based on UHPLC-Q-Exactive MS for data acquisition combined with multipath data mining to analyze the metabolism of phillyrin m/z 533.2017); (2) phillygenin template (m/z 371.1489) and its derivative templates ; (3) enterolactone template (m/z 297.1121). Based on this effective method, some nondiscoverable metabolites could also be screened out from complex background noise.According to literature reports and HREICs search, three compounds  were selected as primary metabolites, which are often found in the metabolites of phillyrin. After data sorting, in negative ion mode, the metabolites screening templates were set as follows: (1) phillyrin template  and 30 Da (CH2O) NLFs in the ESI-MSn spectrum of phillyrin also provided significant help for the identification of metabolites. The cleavage pathway of phillyrin in negative ion mode is presented in To further explore the ESI-MSThe total ion chromatograms (TICs) of urine, plasma, and feces samples after oral administration of phillyrin in rats were obtained using UHPLC-Q-Exactive mass spectrometry. A total of 60 metabolites were found in both positive and negative ion modes by processing the data collected from the UHPLC-Q-Exactive instrument. Among them, there were 31 metabolites in positive ion mode and 33 metabolites in negative ion mode. In addition, after the literature search and comparison, 21 metabolites have been found and detected by predecessors , 31, whi− ion at m/z 579.2072  was eluted at 6.60 min. In contrast to the elution time and fragmentation behavior of the phillyrin standard, M0 could be accurately inferred as the phillyrin [The metabolite M0 producing [M + HCOOH–H]hillyrin .m/z 519.1498 . In the ESI-MS2 spectrum, the DPI at m/z 343 was generated due to the loss of 2CH2 by the phillygenin. While m/z 519 was 176 Da larger than the former, it was inferred that m/z 343 was caused by the absence of the glucuronic acid moiety. Therefore, it was speculated that M17  and M22  were demethylated and carboxylated metabolites of phillyrin, but the position of glucuronic acid group was different.The retention times of the metabolites M17 and M22 were 5.76 and 5.96 min, respectively, and they showed the same molecular ion at − ion at m/z 533.1652 . Similarly, as in the case of M17, the presence of m/z 175 meant the formation of glucuronic acid. While M32 was 14 Da higher than M17, it was speculated that the phillygenin only had one molecule of CH2 removed.The metabolite M32 was eluted at 6.58 min and showed [M-H]− ion at m/z 547.1809 . Similar to the above rule, it was concluded that M36 was the carboxylation product of phillyrin.The metabolite M36 with a retention time of 6.76 min showed [M-H]− ion at m/z 507.2225 . It had a neutral loss of 44 Da, 132 Da, and 44 Da in the ESI-MS2 spectrum, which caused the appearance of m/z 463, m/z 331, and m/z 287. Among them, the 44 Da NLF can be presumed to be CH2O + CH2, while the NLF of 132 Da was formed by the loss of CH2O from the glucosyl group. Therefore, it was concluded that M56 is the dehydroxylation and hydrogenation product of phillyrin.Metabolite M56 exhibited [M-H]+ ion at m/z 305.1772 . According to the DPI at m/z 109 and m/z 93, it could be inferred that two benzene rings of phillygenin were introduced into 2NH2 and CH3, respectively. Interestingly, m/z 205 was 28 Da higher than m/z 177, and m/z 177 was also 28 Da higher than m/z 149, so NLF was assumed to be CH=NH by molecular formula and degree of unsaturation. Therefore, M44 was presumed to be the ammoniated product of phillygenin.The retention time of the metabolite M44 was 7.05 min, which showed [M + H]− ion at m/z 315.0865 . In the ESI-MS2 spectrum, the DPI at m/z 193 demonstrated that one of the phenyl rings of the phillygenin was introduced into 4OH, and the mother nucleus lost 2CH2O. According to the DPI at m/z 124, 137, and 150, the generation of two double bonds could also be proved.The metabolite M2 eluted at 2.49 min showed [M−H]− ion at m/z 329.1021 . The presence of DPI at m/z 137 and 163 in the ESI-MS2 spectrum confirmed the presence of dimethoxyphenyl, while the DPI at m/z109 speculated that 2OH was introduced on the other benzene ring. However, the substitution sites of the two other hydroxyl groups could not be determined.Metabolite M18 was eluted at 5.92 min and showed [M−H]m/z 343.1177 . Similar to the inferred process of M18, after determining the substituents of two benzene rings, it could be concluded that M39 was the product of the loss of 2CH2 by phillygenin.The retention time of the metabolite M39 was 6.79 min, which showed [M−H] ions at + ion at m/z 355.1541 . The DPI at m/z 137, 151 indicated the presence of a bismethoxyphenyl group, while m/z 189 was 112 Da higher than m/z 77 (phenyl), inferring the formation of a double bond in the two five-membered rings. Furthermore, m/z 284 was 30 Da higher than m/z 254, and it was inferred that both were dehydroxyl and dehydrogenated products of phillygenin, except that the position of the double bond was different.Metabolites M38 and M54 eluted at 6.79 min and 8.44 min, respectively, which showed the same [M + H]+ ion at m/z 371.1489 . It was 2 Da lower than the [M + H]+ ion of phillygenin, and combined with DPIs in the ESI-MS2 spectrum, M43 could be identified as the dehydrogenation product of phillygenin.Metabolite M43 was eluted at 6.93 min and showed [M + H]m/z 373.1281  in positive ion mode with a retention time of 9.65 min. The DPI at m/z 373 was 30 Da higher than m/z 343, which meant the loss of CH2O, while m/z 343 was 18 Da higher than m/z 325, indicating that water molecule had been removed. Later, due to the loss of CO NLF, the generation of DPI at m/z 297 was caused. According to this, it could be inferred that after the dehydrogenation and demethylation reaction of the phillygenin, M57 was formed by oxidation. Still, the position of the hydroxyl group could not be determined.The metabolite M57 was extracted in HREIC at − ion was shown at m/z 451.1057 . The DPI at m/z 371 in the ESI-MS2 spectrum can be inferred as phillygenin, and m/z 451 was 80 Da higher than m/z 371, which was a great help for us to conclude that M42 is a sulfonated product of phillygenin llygenin .+ ion at m/z 492.1687 . The DPI at m/z 492 was 137 Da higher than m/z 355, and it was presumed that NLF might be a cysteine-oxygen atom (C3H7NO2S + O). The DPI at m/z 88 (C3H6NO2) was more potent in demonstrating this so that M33 might be the product of the binding of the phillygenin to cysteine.The retention time of M33 was 6.59 min, which showed [M + H]− ions at m/z 261.1146 . From the ESI-MS2 spectrum, the NLF from m/z 262 to m/z 235 might be CH=CH2. Similarly, the DPI at m/z 191 was due to the loss of CH=CH from m/z 217. Moreover, the reason why the m/z 235 was 18 Da higher than the m/z 217 was that one molecule of water was lost. It was presumed that the glucosyl moiety had undergone a reduction reaction, leaving two hydroxyl groups remaining on the ring.The metabolite M58 eluted at 9.90 min produced [M−H]+ ion at m/z 317.0657 . The DPI at m/z 133 was 56 Da (2CO) higher than m/z 77 (phenyl), and m/z 121 was 44 Da (CO + O) higher than m/z 77. It was presumed that the original five-membered ring portion has C=O formation. According to the DPI at m/z 109, it was speculated that 2OH existed in the benzene ring moiety.The metabolite M40 with a retention time of 6.87 min showed [M + H]− ion at m/z 357.1332 . In the ESI-MSn spectrum, the DPIs produced by the cleavage of M47, such as m/z 191, 177, 151, 137, and 123, indicated that the two benzene rings underwent substitution reactions with diverse groups , respectively. At the same time, the NLFs were 30 Da  and 54 Da , it could be inferred that the two benzene rings were mutually connected through a five-membered ring and a double bond. Besides, it was known from the molecular formula that it was difficult to ascertain the substitution site of one OH.The metabolite M47 was eluted at 7.47 min, which had [M−H]+ ion was generated at m/z 331.0181 . The DPI at m/z 162 was 69 Da (C4H5O) higher than m/z 93 , thereby confirming that the five-membered ring has undergone dehydrogenation to form a double bond. According to the molecular formula, in addition to the presence of 2OH in both benzene rings, the substitution site of 2OH could not be identified.The metabolite M48 was eluted at 7.54 min, and [M + H]− ion at m/z 331.1177 . In their ESI-MSn spectra, the NLFs were 30 Da (from m/z 333 to m/z 303) and 28 Da (from m/z 303 to m/z 275); this indicated that there was only one five-membered ring in the molecular structure. Further, product ions such as m/z 109 and m/z 123 stated the substituent groups  on the two benzene rings.The metabolites M8 and M11 with retention times of 4.91 min and 5.08 min, respectively, produced the same [M−H]− ion at m/z 333.1327 . DPIs generated by the continuous neutral loss of CH2O (30 Da) such as m/z 303 and m/z 273 indicated that both five-membered rings were introduced into H2.The metabolite M10 was eluted at 4.96 min and showed [M−H]− ion at m/z 347.1126 . In the ESI-MS2 spectrum, the NLF was 30 Da (from m/z 347 to m/z 317), indicating the presence of a five-membered ring in the structural formula. The DPIs which appeared at m/z 163 and 193 further provided a basis for our inference. According to the DPIs such as m/z 109, 123, and 137, the substitution of two benzene rings could be inferred. Since the substitution sites of the two hydroxyl groups were difficult to judge, M3, M4, and M5 were isomers.Metabolites M3, M4, and M5 eluted at 3.97 min, 4.27 min, and 4.58 min, respectively, showed the same [M−H]− ion at m/z 409.0588 . It was 42 Da lower than M42, presumably owing to the loss of 3CH2. DPIs at m/z 329 and m/z 80 provided strong evidence for this inference. Thus both M14 and M16 were M42 demethylated products, except that the sulfonic acid groups had different binding positions.The retention times of the metabolites M14 and M16 were 5.54 min and 5.69 min, respectively, both of which produced [M−H]+ ions at m/z 357.1332  and m/z 357.1332 , respectively. In the ESI-MS2 spectrum of both, the NLF was 112 Da , so a double bond was introduced in the five-membered ring. However, DPI was generated at m/z 137 in the ESI-MSn spectrum of M30, and it was inferred that the bismethoxyphenyl group was present, and M27 did not have this group, so M27  and M30  were isomers.Metabolites M27 and M30 were eluted at 6.31 min and 6.54 min and showed [M + H]− ions at m/z 359.1490 . The DPI at m/z 344 in the ESI-MS2 spectrum generated m/z 284 by loss of 2CH2O, and then the DPIs such as m/z 192 and m/z 178 could assume that hydrogenation (+2H2) occurred in both five-membered rings and there was one molecular double bond. Further, based on the DPI at m/z 122, the functional groups on the two benzene rings could be deduced.The metabolite M34 with a retention time of 6.65 min showed [M−H]− ion was produced at m/z 375.1074 . In their ESI-MS2 spectra, the NLFs were 28 Da  and 60 Da , respectively. From this, it was presumed that there was a five-membered ring with a double bond and a dimethoxyphenyl group in the structural formula. Combined with the DPIs such as m/z 137, 109, and the degree of unsaturation, the structural formula could be inferred. However, the positions of the three hydroxyl groups were still unidentifiable, and M13 and M19 were positional isomers.The retention times of the metabolites M13 and M19 were 5.30 min and 5.93 min, respectively, and the same [M−H]+ ion at m/z 393.1543  with a retention time of 4.75 min. In the ESI-MS2 spectrum of M6, the DPI at m/z 393 was 93 Da higher than m/z 300, presumably due to the neutral loss of hydroxybenzene. At the same time, the DPI at m/z 300 was 60 Da higher than m/z 240 and 28 Da higher than m/z 272, and it was presumed that NLFs were 2CH2O (60 Da) and 2CH2 (28 Da), respectively. Based on the information obtained, we could conclude that the structural formula of M6 contained hydroxybenzene and dimethoxyphenyl groups, and two of the five-membered rings were hydrogenated to open the ring. The DPIs at m/z 216, 246, and 361 further confirmed the above judgment, and the substitution sites of three other hydroxyl groups could not be determined.The metabolite M6 possessed [M + H]− ion at m/z 437.0901 , and they were eluted at 6.14 min, 6.32 min, and 6.88 min, respectively. Their molecular weight was 14 Da lower than that of M42, so they were inferred to be the demethylation products of M42. In the ESI-MS2 spectrum, the three metabolites had the same DPIs, such as m/z 357, 151, 137, and 80, which proved that their benzene rings had identical substituents . Because the substituents on the benzene rings and the sulfo (-HSO3) substitution site were different, M24, M28, and M41 were positional isomers  isomers .− ion at m/z 453.0850  was 6.16 min. Because its molecular weight was 16 Da higher than M41, it can be speculated that M25 is the oxidation product of M41. The DPIs retrieved from M25's ESI-MSn spectrum, such as m/z 373, 97, and 80, unexpectedly upheld the above guess.The retention time of the metabolite M25 showing [M−H]− ion at m/z 339.2035 . Based on the molecular formula and the calculated degree of unsaturation, M59 was presumed to be a compound in which an alkane containing 15 carbon atoms was replaced by eight hydroxyl groups. Also, the DPIs and NLFs shown in the ESI-MSn spectrum could prove this conclusion.The metabolite M59 was eluted at 26.48 min and showed [M−H]+ ion at m/z 373.1646 . In the ESI-MSn spectrum of M52, through various information, it could be undoubtedly identified as the parent compound of phillygenin llygenin , 35, 36.− ion at m/z 417.1179 , and its retention time was 5.98 min. In the ESI-MS2 spectrum, the NLF was 176 Da (from m/z 417 to m/z 241), and the unsaturated glucuronide group was presumed to be lost, and the DPI at m/z 175 confirmed this. The DPIs at m/z 121 and m/z 103 indicated that the benzene ring was substituted by two methyl groups, so the substitution position of one methyl group could not be determined.The metabolite M23 produced [M−H]+ ion at m/z 377.1953 . In the ESI-MS2 spectrum, NLF was 134 Da (from m/z 377 to m/z 243), and it was presumed to be a derivative of glucose (C5H10O4). According to the DPIs at m/z 172 and m/z 72, the substitution position of four hydroxyl groups could be inferred, of which three hydroxyl groups reacted with the benzene ring, and the other hydroxyl group was substituted on the alkane.The metabolite M7 was eluted at 4.84 min and showed [M + H]+ ion was displayed at m/z 231.1140 . From the DPIs in the ESI-MS2 spectrum of M50, such as m/z 135, 107, and 93, it could be inferred that a hydroxy group was substituted on the benzene ring. Then it could be speculated that another benzene ring had undergone an additional reaction, which itself had been introduced with four hydrogen atoms.The metabolite M50 was eluted at 7.63 min, and the [M + H]+ ion at m/z 273.0759  with a retention time of 7.36 min. In the ESI-MS2 spectrum, the DPIs at m/z 123 and m/z 95 indicated the substitution of two benzene rings . At the same time, the NLF was 28 Da (from m/z 151 to m/z 123 and from m/z 255 to m/z 227), and it was speculated that there were two ketone groups between the two benzene rings.The metabolite M45 produced [M + H]+ ion at m/z 281.1173 . In the ESI-MSn spectrum, the DPIs at m/z 263, 235, 133, and 107 fully demonstrated the double bond formation in the two 5-membered ring structures. Besides, the NLF was 16 Da (from m/z 149 to m/z 133), indicating that there was still a hydroxyl group whose substitution site was unknown.The metabolite M51 was eluted at 7.83 min and showed [M + H]+ ion at m/z 285.0759 . Based on the DPIs located in the ESI-MS2 spectrum, such as m/z 161 and m/z 85, it can be inferred that three consecutive keto groups were present. Meanwhile, the continuous loss of O atom (16 Da) indicated that there are two hydroxyl groups in the other benzene ring.The metabolite M37 with a retention time of 6.77 min showed [M + H]+ ion at m/z 295.0964 . According to the continuous neutral loss of CO (28 Da) (from m/z 277 to m/z 249 and from m/z 249 to m/z 221) and the DPI at m/z 186, it could be presumed that there were two double bonds in the two five-membered ring structure. Furthermore, the continuous loss of water molecules (18 Da) further proved the existence of dihydroxyphenyl.The metabolite M20, which was eluted at 5.93 min, had [M + H]+ ion at m/z 298.0962 . In the ESI-MS2 spectrum of M1, the NLFs were 26 Da (from m/z 189 to m/z 163 to m/z 137), and it was presumably caused by the continuous loss of CN. What is more, the substitution site where a hydroxyl group should also exist in the structural formula could not be determined. Therefore, there were three cyano groups and one hydroxyl group in the structural formula.The retention time of the metabolite M1 was 1.61 min, which showed [M + H]+ ion at m/z 313.1070 . According to the DPIs in the ESI-MS2 spectrum, such as m/z 277, 249, 123, 161, and 173, could be presumed the substitution of two benzene rings  could be presumed and the existence of a five-membered ring and two double bonds between them. Also, according to the molecular formula, it was known that there was still one hydroxyl substitution site that could not be identified.The metabolite M21 was eluted at 5.94 min and showed [M + H]+ ion at m/z 315.1227 . In the ESI-MS2 spectrum of M53, the DPIs at m/z 93, 109, 167, and 189 could fully clarify the substituents  on the two benzene rings. At the same time, the NLF was 60 Da (from m/z 315 to m/z 255), which was presumed to be due to the continuous loss of CH2O, so there were two five-membered rings in the structural formula.Metabolite M53 has a retention time of 8.39 min, which had [M + H]+ ion at m/z 317.1384 . Analogous to the above, based on the DPIs in the ESI-MS2 spectrum, such as m/z 123, 149, 151, 163, and 175, the substituents (OH + OCH3) on the two benzene rings were presumed. It was inferred from the degree of unsaturation that the linear alkane between the two benzene rings undergone a dehydrogenation reaction to form a double bond. At the same time, it could be known from the molecular formula that another OH substitution site could not be determined.The metabolite M9 was eluted at 4.95 min, and it showed [M + H]+ ion at m/z 341.1384 . Among them, in the ESI-MS2 spectra of M15, M26, and M29, DPIs at m/z 137 and m/z 187 indicated the presence of dimethoxyphenyl and methoxyphenyl. At the same time, NLFs were 28 Da (from m/z 291 to m/z 263) and 70 Da (from m/z 341 to m/z 271), which proved that there were a five-membered ring and two double bonds between the two benzene ring structures. Additionally, it was difficult to determine the position of one hydroxyl group. Therefore, it was inferred that the three metabolites were isomers because the substitution positions of methoxy and hydroxyl groups were different. However, in the ESI-MSn spectra of M31 and M46, the substituents  on the two benzene rings could be presumed based on the DPIs at m/z 123, 151, and 161. Meanwhile, the NLFs were 30 Da  and 28 Da , and the existence of two five-membered rings and the formation of a double bond were further deduced. Because the position of the methoxy group was difficult to determine, M31 and M46 were positional isomers.The metabolites M15, M26, M29, M31, and M46 were eluted at 5.62, 6.21, 6.38, 6.55, and 7.44 min, respectively, and they all showed the same [M + H]− ion at m/z 345.0969 . DPIs in the ESI-MS2 spectrum, such as m/z 109 and 137, revealed the structure of two benzene rings . The neutral loss of 60 Da (2CH2O) resulted in the formation of DPI at m/z 285, indicating that the two five-membered rings remained unchanged. Also, there was still no known substitution site for a hydroxyl group, so M12 was a demethylation and oxidation product of phillygenin.The retention time of the metabolite M12 was 5.15 min, and it had [M−H]− ion at m/z 377.0689 . In the ESI-MS2 spectrum, the NLF was 81 Da (HSO3), which was presumably due to the neutral loss of the sulfo group. At the same time, the DPIs at m/z 107 and m/z 189 revealed the existence of the substituents on the two benzene rings  and the presence of two double bonds between the benzene rings.The metabolite M35 was eluted at 6.69 min and produced [M−H]− ion at m/z 271.1329 . In the ESI-MSn spectrum, the NLF was 60 Da (from m/z 271 to m/z 211), and it was inferred that the two five-membered rings undergo hydrogenation (+H4) to open the ring. The DPIs at m/z 151 and m/z 165 proved that the benzene ring had an addition reaction (H2) and was replaced by 2OCH3. Another OH substitution site was challenging to identify.The metabolite M49, which was eluted at 7.61 min, showed [M−H]+ ion at m/z 273.1486 . The DPIs in the ESI-MS2 spectrum, such as m/z 179, 137, and 95, clarified the structure of the two benzene rings . Based on the established molecular formula, it could be inferred that the fabric between two benzene rings was a linear alkane containing four carbon atoms.The metabolite M55 with a retention time of 8.49 min showed [M + H]In our study, a total of 60 metabolites (including the prototype compound) were detected and identified in rats that were orally administered phillyrin. The proposed metabolic pathways of forsythiaside are shown in in vivo metabolites of phillyrin. Firstly, the combination of UHPLC and Q-Exactive MS overcame many of the shortcomings of traditional triple quadrupole mass spectrometry, and the extremely high resolution could significantly eliminate the interference of the sample matrix. Secondly, the formation of the data set mainly depended on a variety of data mining methods, such as high extracted ion chromatogram (HEIC), diagnostic product ion (DPI), neutral loss filtering (NLF), and isotope pattern filtering (IPF). Finally, accurate qualitative identification of compounds could be achieved based on the precise relative molecular mass of each chromatographic peak in the mass spectrum, the ion fragment information of the secondary mass spectrum, the fragmentation rule of the mass spectrum, and the chromatographic retention time. The results showed that the primary biological reactions of phillyrin in vivo included methylation, hydrogenation, sulfonation, glucuronidation, demethylation, dehydrogenation, ring cleavage, and their composite reactions. Among them, the biological activity of some specific metabolites was unknown. In summary, this study provided vital information to the research field of phillyrin metabolism and had important value significance for studying the mechanism of action of phillyrin and monitoring of drug content.After oral administration of phillyrin to rats, metabolites in plasma, urine, and feces of rats were studied comprehensively. Its innovation lay in the use of a new comprehensive strategy to screen and identify 60


We investigated the association between muscle strength and the prevalence of advanced fibrosis among individuals with non‐alcoholic fatty liver disease (NAFLD) using a nationwide cross‐sectional survey.N = 14 861), with sample weights applied. Muscle strength was quantified as the handgrip strength divided by the body mass index (BMI); low muscle strength (LMS) was defined as the lowest quartile (Q1) of the handgrip strength/BMI for our sample population. NAFLD was defined as hepatic steatosis index >36. Advanced fibrosis was defined as a fibrosis‐4 index score ≥1.30 (FibrosisFIB4).Individuals, 20 to 79 years of age, from the Korean National Health and Nutrition Examination Surveys (KNHANES) from 2014 to 2016 were selected . In a crude analysis, the LMS was associated with an increased prevalence of NAFLD , which remained significant even after adjustment for age, sex, obesity, insulin resistance, diabetes, hypertension, dyslipidaemia, and high‐sensitivity C‐reactive protein . In this logistic regression model, the prevalence of NAFLD decreased by 24% with each quartile increment in muscle strength . Among individuals with NAFLD (n = 2092), LMS was significantly associated with the presence of advanced fibrosis (FibrosisFIB4) independently of age, sex, obesity, diabetes, hypertension, dyslipidaemia, and high‐sensitivity C‐reactive protein , which lost its statistical significance after additional adjustment for insulin resistance.The mean age of the study population was 45.6 ± 0.2 years, and 42.4% were male. As muscle strength increased, the mean BMI and age decreased accordingly, and the proportions of diabetes, dyslipidaemia, hypertension, and obesity decreased significantly (Low muscle strength is independently associated with NAFLD. The significant association between LMS and advanced fibrosis in NAFLD may be mediated through insulin resistance. Sarcopenia is defined as a progressive decrease in muscle mass and function.To date, the association between NAFLD and sarcopenia has been principally evaluated from a perspective of low skeletal muscle mass rather than the quality of muscle function.In the current study, we investigated the association between muscle strength and the prevalence of NAFLD through an analysis of a nationwide representative cross‐sectional survey dataset. In addition, the association between muscle strength and advanced fibrosis among individuals with NAFLD was also analysed.We performed a retrospective analysis of the 2014 to 2016 data from the Korean National Health and Nutrition Examination Surveys (KNHANES). The KNHANES involves a nationwide cross‐sectional health examination and survey performed by the Korea Centers for Disease Control and Prevention in the Ministry of Health and Welfare, with details having been previously reported.FigureAmong a total of 23 080 participants enrolled in the KNHANES between 2014 and 2016, we included 14 861 individuals, 20 to 79 years old, whose handgrip strength (HGS) data were available. The exclusion criteria were as follows: alcohol consumption >210 g/week for men and >140 g/week for women,2Diabetes mellitus (DM) was defined as an 8 h fasting plasma glucose level ≥7.0 mmol/L (126 mg/dL) or glycated haemoglobin level ≥48 mmol/mol (6.5%), or use of anti‐diabetic medication including insulin.1 and Q4 being the lowest and highest quartiles of HGS/BMI, respectively. The Q1 was defined as the group of subjects with low muscle strength (LMS).Muscle strength was quantified by the HGS, assessed using a digital grip strength dynamometer , as previously reported.Non‐alcoholic fatty liver disease was defined using previously well‐validated fatty liver prediction models: the hepatic steatosis index (HSI),9/L] × (ALT [U/L])1/2 and (ii) BARD scoreFIB4)BARD).n = 2092) were included in analysis for advanced fibrosis.Among individuals with NAFLD, as defined by the HSI, advanced fibrosis was assessed using the following prediction models: (i) fibrosis‐4 index (FIB‐4)P value <0.05 considered significant for all tests.Variables are reported as the mean ± standard error (SE) or the prevalence ± SE (%). Linear regression or logistic linear regression analysis was used to compare the clinical variables according to muscle strength, adjusted for age, sex, obesity, IR, and other metabolic parameters. Sampling weights were applied to all analyses. Statistical analyses were performed using IBM SPSS Statistics , with a 1, Q2, Q3, and Q4, respectively , 29.7 kg , 33.4 kg , and 37.5 kg , and muscle strength (HGS/BMI) was 0.90 , 1.19 , 1.40 , and 1.71  in the Q1, Q2, Q3, and Q4, respectively . After adjustment for age, sex, obesity, DM, HTN, DL, and elevated hs‐CRP level, the association between LMS and NAFLD remained significant , 30.5% , 20.3% , and 7.5% (SE 0.5%) in the QTable1) was significantly higher in all age strata groups than in the other groups   Figure.Figure1 . For every 1 quartile (1Q) increment in muscle strength, the prevalence of NAFLD decreased by 51% . The prevalence of NAFLD was significantly lower even in the Q2 group  compared with the Q1 group . After adjustment for age, sex, obesity, DM, HTN, DL, elevated hs‐CRP level, and IR, this quantitative association between muscle strength and the prevalence of NAFLD remained significant (Model 4 in TableP < 0.001).A dose–response relationship was observed between muscle strength and NAFLD Figure; the pren = 2092). For these patients with NAFLD, both FIB‐4 and BARD scores were calculated to detect advanced fibrosis. The prevalence of advanced fibrosis in each quartile (Q1–Q4) showed a decreasing trend as muscle strength increased , 11.2% , 8.0% , and 2.6%  (P < 0.001); FibrosisBARD, 63.9% , 50.3% , 42.4% , and 29.7%  (P < 0.001).To investigate the association between muscle strength and advanced fibrosis, only individuals with NAFLD were selected , which remained statistically significant after adjustment for age, sex, and obesity  than in those with HOMA‐IR ≥ 2.5 , and the same trend held true for FibrosisBARD  had a significantly lower risk of NAFLD than those with LMS. Among those with NAFLD, LMS showed a significant association with advanced fibrosis independent of age, sex, and other metabolic derangements. However, additional adjustment for IR attenuated this association; LMS was not associated with FibrosisFIB4 after adjustment for IR.In the current study, muscle strength was inversely associated with the prevalence of NAFLD, irrespective of age. LMS was an independent risk factor for NAFLD regardless of age, sex, obesity, DM, HTN, DL, hs‐CRP level, and IR with the odds of NAFLD among individuals with LMS reaching 1.66. Moreover, there was a dose–response relationship between muscle strength and the prevalence of NAFLD; even individuals in the second lowest muscle strength group  were subjected to HOMA‐IR tests, which might account for the lack of statistical significance in the association between LMS and advanced fibrosis adjusted for IR. Second, because of the cross‐sectional nature of the current study, causality cannot be determined between LMS and incident NAFLD or advanced fibrosis. Therefore, further prospective longitudinal cohort studies should be conducted to validate whether LMS is the real culprit of NAFLD and advanced fibrosis. Finally, because of insufficient muscle mass data in the current KNHANES analysis, we could not confirm whether the association between LMS and NAFLD persists independently of muscle mass. Although a previous study investigated the association between muscle mass and liver fibrosis using the KNHANES data between 2008 and 2011,Collectively, we demonstrated the association between LMS and NAFLD or advanced fibrosis from the nationwide survey representing Korean population. LMS was significantly associated with an increased prevalence of NAFLD independently of age and BMI and was also closely associated with increased IR and higher prevalence of metabolic diseases. The association between LMS and advanced fibrosis in NAFLD subjects was found to be independent of metabolic diseases, such as DM, DL, and obesity, which seemed to be mediated through IR. Considering the cross‐sectional nature of the current study, further investigations using a large‐scale, histologically confirmed NAFLD cohort are warranted to draw a definite conclusion on the association between LMS and advanced fibrosis in NAFLD. Given that a substantial number of NAFLD subjects are non‐obese and there has been no approved pharmacotherapy, our study suggests the therapeutic role of resistance training, especially in lean subjects with NAFLD.This work was supported by a public healthcare research grant‐in‐aid from the Seoul Metropolitan Government Seoul National University Boramae Medical Center (04‐2019‐1), the National Research Foundation of Korea (NRF) grant funded by the Korean government  (2016R1D1A1B04934590), and the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI) funded by the Ministry of Health and Welfare, Korea (HI17C0912).S.K. and B.K.K. designed the study and collected and analysed the data. S.K., M.K.M., W.K., and B.K.K. interpreted the data and wrote the manuscript. W.K. and B.K.K. critically reviewed and approved the final version of the manuscript.None declared.Table S1. Prevalence of nonalcoholic fatty liver disease according to muscle strength quartiles in 10‐year age strataClick here for additional data file.


Scanning and transmission electron microscopy observations indicate coalescence in granular colonies of La0.7Ca0.23Sr0.07MnO3 nanoparticles to conform nanofibers. Magnetic entropy change is tuned due to heat treatments at 1173 K with maximum values of 1, 1.82 and 2.51 J/kgK for applied external magnetic fields of μ0H = 1, 2 and 3T, respectively, with a maximum magnetic entropy difference at a Curie temperature of 293 K . Additionally, for a magnetic field, ~μ0H = 3 T values of 49, 95 and 143 J/kg for 973, 1073 and 1173 K heat-treated samples were obtained.We present experimental evidence about the magnetocaloric tuning effect in one-dimensional nanostructure fibers mixed-valence manganite as synthesized by electrospinning techniques and under heat treatments of 973, 1073 and 1173 K. The stoichiometry obtained is La MCE technology aims to replace traditional refrigeration systems mainly due to its high energy efficiency, compact design and ecological interplay with the environment . When esi et al. . Generaltructure . Also, dotubes d~0 nm and eratures ,12. Here0.7Ca0.23Sr0.07MnO3 were synthesized using La, Ca, Sr and Mn acetates as precursors. All precursors were dissolved in deionized water at 0.1 molar and subsequently mixed with 20% of polyvinylpyrrolidone (PVP) to form a homogenous solution. Using a stainless steel 22-gauge needle with working distance of 15 cm between needle and aluminum substrate, 0.1 mL/h fluid flow and 15 kV as set conditions; nanofibers were produced with deposits via a Tong Li Tech Co.® electrospinning equipment model TL-Pro©. The as-deposited nanofibers were collected and subjected to heat treatments of 973 K, 1073 K and 1173 K (ramp rate of 1 K/min) for 1.5 h, as described previously by Burrola et al. [® and transmission electron microscopy (TEM) with a field emission 2200-JEOL® equipped with CCDcamera, STEM unit, high-angle annular dark-field (HAADF) detector and X-Twin lenses. The sample was dispersed on isopropanol and drop cast onto 200 mesh Lacey Formvar Carbon TEM grids. Measurements of magnetization isotherms were carried out in a Quantum Design Versalab®, with a vibrating sample magnetometer attachment.The nanofibers with stoichiometry Laa et al. . Phase f0.7Ca0.23Sr0.07MnO3 nanofibers under thermal treatment of (a) 973 K, (b) 1073 K and (c) 1173 K. The crystallographic information for all refinements was acquired using 56636 ICSD card, which corresponds to single-phase orthorhombic structure Pnma space group and crystallite size of 48 nm 50 nm and 62 nm for (a) 973 K, (b) 1073 K and (c) 1173 K, respectively. One can conclude that crystallite size increases due to heat treatments, possibly due to coalescence phenomena, as confirmed by TEM measurements. A summary of Rietveld refinement is presented in Using information from powder X-ray diffraction patterns performed at room temperature, the Rietveld refinements was calculated using Full Proof program. 0.7Ca0.23Sr0.07MnO3 nanofibers, transmission and scanning electron microscopy were applied. Results indicate nanofibers and agglomeration with no beads fomation, which is typical due to injection rate (0.1 mL/h) of the as-synthesized solution, along with thermal treatments, as observed clearly in 0.7Ca0.23Sr0.07MnO3 nanocrystallites to conform nanofibers, in agreement with Zhou et al. [In order to determine morphology and size of Lau et al.  and as poHμ = 3T. Results of magnetization isotherms for (a) 973 K, (b) 1073 K and (c) 1173 K are presented in CT) [From curves of magnetization as a function of magnetic field measured at constant temperatures, magnetic entropy change was estimated for the three samples. The measurements were carried out in temperature range 180–380 K and under applied magnetic field up to δM is change on magnetization, and T and H are temperature and applied magnetic field, respectively. Due the discrete data of isothermal magnetization acquired from experimental measurements, a numerical approach to thermodynamic Maxwell relation using Equation (2) as described by Khlifa et al. [iM and i+M1 are the magnetization values measured at iT and i+T1 temperature, respectively, for a corresponding applied magnetic field μ0iH. Moreover, to identify any efficiency of a magnetocaloric material in a refrigeration cycle, it is important to consider the refrigerant cooling power (RCP) parameter. The expression to obtain RCP is displayed by Equation (3), in agreement with Iqbal et al. [MmaxS is the maximum magnetic entropy change value and FWHMδT the full width at half maximum of the magnetic entropy curve. The magnetic entropy change as a function of temperature is presented in μ0H = 1, 2 and 3 T, where a maximum magnetic entropy change occurs near 293 K and remains invariable when applied magnetic field increases. This temperature is pointed out as CT, where the magnetic phase changes from a ferromagnetic to a paramagnetic state as discussed for μ0H = 0 T to 3 T.Using the magnetization isotherms data as obtained for all samples, the magnetic entropy change can be calculated using thermodynamic Maxwell relation, as described by Tola et al. , as folla et al.  is takenl et al. :(3)RCP=μ0H = 1, 2 and 3 T respectively, as presented in μ0H = 1, 2 and 3 T.This behavior can be described with the Maxwell relation expressed in Equation (1), as the change in magnetization with respect to temperature is proportional to the magnetic field applied. The maximum values of magnetic entropy change for sample at 973 K are 0.28, 0.53 and 0.74 J/kgK for applied magnetic fields of C~293 K and increase when compared with, sample under thermal treatment of 973 K, which is strongly correlated with coalescence among colonies of La0.7Ca0.23Sr0.07MnO3 to conform nanofibers. Also, RCP increases with values of 29, 62 and 96 J/kg found for applied magnetic fields of 1, 2 and 3 T. At last, MEC values for sample at 1173 K are displayed in 0.7Ca0.23Sr0.07MnO3 nanofibers under thermal treatment (1173 K) possess maximum values of magnetic entropy change and RCP, when compared to the other two samples at 973 K and 1073 K, respectively. The latter are attributed to higher applied temperature and total time of heat treatment due to granule interconnexion from coalescence of La0.7Ca0.23Sr0.07MnO3, attributed to nanoscale phenomena, which improvesmagnetic homogeneity causing long-range ferromagnetic order, as extensively described for manganites by Baaziz et al. [z et al. . MmaxS, FWHMδT and RCP parameters for all samples are summarized in μ0H = 1, 2 and 3 T. An increase of magnetic entropy change and RCP is clearly observed, caused by heat treatments and confirmed when applied magnetic field is present. When closely analyzing the FWHMδT parameter, it is possible to prove that −M∆S peak narrows as processing temperature increases. Interestingly, RCP increases even when the FWHMδT becomes smaller if the values are compared for same applied magnetic field, this could be attributed to an improvement of magnetic homogeneity and long-range ferromagnetic order [μ0H = 3T.The results for ∆ic order . A compaμ0H = 2 T) or 120 J/kg (μ0H = 2 T), both for Gd element, as described extensively in the literature by Wang et al. [In addition, Gd metal represents the benchmark material for refrigeration at room temperature because of its suitable magnetocaloric and RCP values, as extensively described by Khlifa et al. ,15. The g et al.  and by Pμ0H⁄M vs. M2) displayed in 0.7Ca0.23Sr0.07MnO3 nanofibers with heat treatments at 973 K, 1073 K and 1173 K, which is a proof of second-order magnetic phase transition. The second-order nature of the magnetic phase transition around room temperature indicates that no thermal or magnetic hysteresis are present for this system [0.7Ca0.23Sr0.07MnO3 nanofibers can be considered an interesting alternative for magnetic refrigeration at room temperature.The magnetic phase transition order for all samples was determined by Arrot plots  was identified on samples under thermal treatment of 973, 1073 and 1173 K, by Rietveld refinement. Magnetic entropy change and RCP increases as function of heat treatment temperature. Then, magnetic homogeneity was improved via coalescence effect and a long range ferromagnetic order came up sequentially with higher temperatures. Maximum magnetic entropy change values of 0.74, 1.65 and 2.51 J/kgK for 973, 1073 and 1173 K, respectively were obtained near Curie temperature of ~293 K. The relative cooling power (RCP) with maximum values of 49, 95 and 143 J/kg was obtained for 973, 1073 and 1173 K samples, respectively. A second-order magnetic phase transition was determined according to Banerjee criterion in Arrot plots. The results demonstrate that the dimensionality and composition of La0.7Ca0.23Sr0.07MnO3 nanofibers can be convenient for magnetic refrigeration at room temperature and low magnetic fields, and for potential applications in low dimension electronics, sensors and other important devices.A successful synthesis of La


In addition, no significant differences were found between the %1RM achieved directly and the %1RM obtained by the equation calculated from the linear regression . Conclusions: these results indicate that movement velocity can estimate the relative load in bench press and leg press exercises in persons MS.Objectives: This study aims to analyse the validity (agreement between two methods) of the movement propulsive velocity (MPV) as an indicator of relative load in leg press (LP) and bench press (BP) exercises in persons with multiple sclerosis (MS). Methods: 18 persons with MS  = 44.88 ± 10.62 years; body mass = 67.19 ± 10.63 kg; height = 1.66 ± 0.07 m; Expanded Disability Status Scale (EDSS) = 3.12 ± 1.73) performed an incremental loading test in BP and LP exercises in two separate sessions. Individual determination of the one-repetition maximum (1RM) and full load-velocity profile were obtained for each participant. Results: a significant linear relationship was observed between the %1RM load and the MPV in LP (%1RM = −133.58 × MPV + 117.44; r Persons who have multiple sclerosis (MS), an autoimmune disorder that leads to myelin and axon loss, present a variety of symptoms, such as spastic paresis , impaireResistance training in MS has shown not only improvements in muscle strength or functional capacity , but alsAdditionally, MS patients’ conditions may vary daily because of environmental effects, such as temperature ,16; thisMean propulsive velocity (MPV) has been used to estimate the 1RM due to its high linear correlation with the 1RM in different exercises, e.g., bench press  or leg pA total of 18 persons with MS were recruited from the local MS association . All parAll training and testing sessions were completed in the UCAM (Universidad Católica San Antonio de Murcia) Sports Center . A cross-sectional study was developed to analyse whether movement velocity could be a valid and reliable method for estimating the percentage of 1RM (relative load) during the leg press and bench press exercises in persons with MS. The chosen exercises were the bench press and leg press as two of the most important exercises for improving both upper and lower body strength ,24. Part−1 determined load increase of 20 kg; 0.80 m × s−1 ≥ MPV ≥ 0.30 m × s−1 determined load increase of 10 kg; and MPV < 0.30 m × s−1 determined load increase of 5 kg. Participants performed three repetitions with light loads, two repetitions with medium loads, and one repetition with heavy loads. Light loads were considered (MPV > 1.00 m×s−1), medium loads (1.00 m × s−1 ≥ MPV ≥ 0.45 m × s−1) and heavy loads (MPV < 0.45 m × s−1). Participants performed three repetitions with light loads, two repetitions with medium loads, and one repetition with heavy loads. Five minutes were given to rest between sets. The highest MPV of each load was analysed. To perform a correct measurement of the MPV, the eccentric phase of the movement lasted 2 s and was controlled by a metronome, until the participant reached 90° of knee flexion, position in which a stop was placed so that knee flexion of more than 90° would not occur. In this position, a concentric phase was performed as fast as possible. Participants performed an average of 9.23 ± 4.54 sets. A horizontal leg press machine  was used for the study. To measure the MPV of the movement, a linear position transducer  was used. The device was fixed to the machine platform.Initially, a standardised warm-up was performed that included five minutes on a cycloergometer, followed by dynamic stretching of the lower-body and one set of 10 repetitions against 10 kg during the leg press exercise. The participants started with an external load of 30 kg that was increased until they reached the maximum load that they were able to lift in 1RM. The load increases depended on the mean propulsive velocity (MPV) achieved during the set, following the recommendations of previous studies : MPV > 0−1 determined load increase of 5 kg; 0.80 m × s−1 ≥ MPV ≥ 0.30 m × s−1 determined load increase of 2.5 kg; and MPV < 0.30 m × s−1 determined load increase of 1 kg. Participants performed three repetitions with light loads, two repetitions with medium loads, and one repetition with heavy loads. Light loads were considered (MPV > 1.00 m × s−1), medium loads (1.00 m × s−1 ≥ MPV ≥ 0.45 m×s−1) and heavy loads (MPV < 0.45 m × s−1). Five minutes were given to rest between sets. The highest MPV of each load was analysed. To make a correct measurement of the MPV, the eccentric phase of the movement was controlled until the barbell was in contact with the chest. In this position, a concentric phase was performed as fast as possible until their elbows reached full extension. Participants performed an average of 10.21 ± 4.56 sets. A Smith machine  was used for this exercise. To measure the MPV of the movement, a linear position transducer  was used. The device was fixed to the left side of the barbell.Initially, a standardised warm-up was performed with five minutes on an arm ergometer, followed by dynamic stretching of the upper-body and one set of 10 repetitions against 5 kg (mass of the unloaded barbell) during the bench press exercise. The participants started without an external load (only with the barbell) and the external load was added until they reached the maximum load that they were able to lift in 1RM. The load increases depended on the MPV achieved during the set: MPV > 0.80 m × sr). The magnitude of the correlations was assessed according to Hopkins et al. [2), F-statistic, and error of estimation (SEE) [The statistical analyses were conducted using Jamovi. Available online: jamovi.org (accessed on 15 March 2020)  and statistical package SPSS  for Windows. Before the data analysis, the Kolmogorov-Smirnov test was used to determine the normal distribution of the variables. The relationship between relative load (%1RM) and MPV were determined by linear regression models . The relationships were initially assessed by Pearson’s coefficient correlation  . Subsequon (SEE) . Bland-Aon (SEE) . Differen = 156, r = −0.911, p < 0.001; bench press, n = 195, r = −0.925, p < 0.001). Additionally, a statistically significant linear regression model was observed . The linear regression model showed the following regression equations stimate the %1RM from MPV in people with MS Leg press %1RM = −133.58 × MPV + 117.44 Bench press %1RM = −95.66 ×MPV + 115.26 . Moreover, Intra-class correlations showed an excellent mean absolute agreement  = 0.915, CI = 0.886–0.938; bench press, ICC = 0.919, CI = 0.894–0.938).In addition, the mean difference between the two methods was not statistically different from 0 .r2 ≥ 0.94) [r2 = 0.84 and SEE = 9.4% 1RM in leg press; r2 = 0.86 and SEE = 9.8% 1RM in the bench press) than previous studies with young individuals. Marcos-Pardo et al. [r2 = 0.83) and leg press (r2 = 0.91) and a lower MPV associated with a given %1RM in older women in comparison with young individuals. This fact could be related to the impairment in power production in ageing individuals [Regarding the precision of the load-velocity relationship, previous studies in young individuals reported a strong association in the bench and leg press ( ≥ 0.94) ,21,25,31o et al.  found a ividuals . Therefoividuals , indicatr2 = 0.96) and older women [r2 = 0.91). In this sense, the relationship reported here was weaker  than the previous results.Most training programs in MS populations involve lower limbs . This faer women  (r2 = 0.r2 = 0.94–0.97) in young individuals [r2 = 0.86) than the results in healthy people obtained previously. Nevertheless, our results are in agreement with a recent study with older women (r2 = 0.83) [On the other hand, the bench press is a common exercise in resistance training routines for healthy people  and persividuals ,25,30,37 = 0.83) . One pos = 0.83) . Hence, Moreover, MPV results with each %1RM were slower than those reported previously for young individuals  and closWe acknowledge some limitations which should be considered for data interpretation. The main limitation was the small number of persons with MS, which took part in the research. Another limitation is that the standard error of the estimate is greater than ideal in both exercises. Furthermore, another limitation was that the study included both sexes (men and women), while previous research suggested that sex could modify the precision of the % 1RM estimate . AnotherMPV can be used to prescribe and control the load (%1RM) during resistance training sessions in persons with MS and a disability status <6. Therefore, the MPV is a reliable and valid method. These findings may help coaches and researchers who want to monitor the training load in persons with multiple sclerosis. Therefore, the main strength of the present study is the novelty of the study and the practical application of the results to the real clinical field. Physicians and coaches should acknowledge a very close relationship of movement velocity and %1RM that can be used as a training control in MS patients, avoiding the problems associated with direct assessment of 1RM .


Relationships between aμ, μ′s and SSC, and soluble sugars contents, were investigated. The result showed that SSC, the content of total soluble sugars (TSS), fructose, glucose and sucrose were all decreasing after storage, and the same trend appeared in the change of aμ and μ′s. In the whole wavelength range, both aμ and μ′s were positively related to SSC and soluble sugars contents. The correlations between aμ and SSC, and soluble sugars contents, showed increasing tendencies with increasing wavelengths, while for μ′s, correlations had the opposite trend. The strongest correlations between aμ and SSC, and soluble sugars contents, were observed in the correlation of aμ with sucrose, with an r of 0.934. Furthermore, a partial least square (PLS) model for sucrose based on aμ was built with the coefficient of determination of prediction (pR2) and the root mean square error of prediction (RMSEP) of 0.851 and 1.047, respectively. The overall results demonstrate that optical properties at the range of 905–1650 nm could be used to evaluate SSC of apples and this may due to the strong correlation between sucrose content and aμ.Soluble solid content (SSC) is regarded as the most significant internal quality associated with the taste and maturity in fruits. Evaluating the relationship between the optical properties and soluble sugars facilitates exploration of the mechanism of optical techniques in SSC assessment. In this research, absorption coefficient ( Apple fruit is highly valued by consumers for its crispy texture and sweet and sour taste. Soluble sugars in apple fruit largely affect sweetness, and they are the main components of soluble solids . CompareLight transfer in tissues contains absorption or scattering, and the process of interaction mainly depends on the different chemical components and structures of tissues. By collecting light that is reflected back from or transmitted through the tissue based on NIR and HSI techniques, the chemical as well as structural characteristics of tissues can be acquired . The preaμ) and reduced scattering coefficient (μ′s) [aμ and μ′s spectra of pear tissues in 400–1150 nm. Using a single integrating sphere, Wang et al. [The optical parameters measurement resulted in separate and quantized information of the absorption and scattering properties by calculating the absorption coefficient (nt (μ′s) ,16. The nt (μ′s) ,18, spatnt (μ′s) , time-rent (μ′s)  and freqnt (μ′s) . In addint (μ′s) ,23. Suchnt (μ′s) ,25,26,27nt (μ′s)  applied g et al.  estimateaμ around 525 and 675 nm were affected by anthocyanin and chlorophyll. In addition, aμ at 630 nm is attributed to total galacturonic acid (GA) in residue insoluble pectin (RIP), with an r of 0.64 [aμ and μ′s at 675 nm are noticed for area and equivalent diameter of apple tissue cells (r = 0.581–0.941) [Many studies in Vis-NIR (400–1100 nm) have been conducted. The findings showed that 1–0.941) . Nowaday1–0.941) ,32. Thus1–0.941) ,34. The 1–0.941) . Howeveraμ and μ′s of apple fleshes in longer wavelength range NIR (905–1650 nm) were calculated based on the AIS system with the IAD method, and internal quality (SSC and the contents of soluble sugars) was measured. Finally, relationships between aμ, μ′s and SSC, the contents of total soluble sugar (TSS), fructose, glucose and sucrose were investigated, then the potential for predicting the SSC was evaluated.Therefore, this research aimed to comprehensively clarify the mechanism of detecting SSC by optical technology and provide information for accurately developing improved SSC detectors of Fuji apple during storage by optical technology. In the work, the The Fuji apples were provided by an orchard in Xuzhou, Jiangsu Province, China. In total, 60 apple samples were stored at 0 ± 1 °C with relative humidity (RH 95%) in normal atmosphere (NA) for 150 days. The measurement took place during 0–150 days, at 30-day intervals and in total 10 individual apples were picked and measured randomly after placing at room temperature for 5 h on each occasion. Another 60 samples were stored at 25 °C with RH 95% in NA for 50 days, and 10 apples were picked from 0 day at about 10-day intervals up to 50 days.aμ and μ′s. The diffuse reflectance and diffuse transmittance of the samples, reference and dark noise were needed to calculate aμ and μ′s, and the spectral signals (905–1650 nm) were collected by a spectrometer . Different measurement configurations were presented in The automatic integrating sphere system with IAD was employed to measure the aμ and μ′s was validated with both pure water and 1% Intralipid® . The main ingredients were soybean oil, glycerin and egg yolk phospholipids. In addition, the particle size distribution in the solution was relatively uniform and stable, also very close to the main scattering particles (chylomicrons) in biological tissues. Thus, it was taken as standard scattering resolution. The scattering resolution was established by diluting 20% stock solution with water. The reference aμ and μ′s values were described by Van et al. [The accuracy of the estimated n et al.  and Dengn et al. . The pern et al. .According to the Monte Carlo simulation of Ma et al. , the penv/v). The flow rate was 0.8 mL/min at 40 °C. Different concentrations of the sugar standard solution were made for glucose, fructose and sucrose, then the content of the three sugars in the samples was established by comparing each peak retention time and peak area with those of the standard.The juice of the fresh slices was obtained to assess the SSC by a hand-held refractometer  after spectral measurement. The measurement range, resolution and accuracy of the instrument were 0.0–53.0% °Brix, 0.1% °Brix and ±0.2% °Brix, respectively. In addition, fructose, glucose, and sucrose contents were quantified by high performance liquid chromatography (HPLC) as described further in Wei et al. . For eacp < 0.05 using the SPSS Statistics 20 package (IBM). The correlation between the contents of soluble sugars and soluble solids was investigated using the Pearson method of analysis with significant p < 0.05 in IBM SPSS 20. The partial least square regression (PLS) models of SSC, TSS and soluble sugars contents were built based on aμ and μ′s separately using MATLAB R2010b® in order to choose the most important soluble sugar parameter that determined optical properties of apple flesh. Finally, the model performance was evaluated by the determination coefficient of prediction (pR2) with the root mean square error of prediction (RMSEP), and the determination coefficient of calibration (cR2) with the root mean square error of calibration (RMSEC).All the quantitative comparations between the different soluble sugars of the examined apple flesh were subjected to Variance analysis, which were tested for aμ of water obtained by AIS and aμ reported by Deng et al. [aμ values at 905–1600 nm ranged from 0.03% to 10.25%. μ′s values of 1% Intralipid based on AIS. A comparison of the measured μ′s and reference value based on Mie theory revealed that both profiles showed a downward trend with the increase in wavelengths [aμ and μ′s after 1600 nm clearly increases. This is due to a low signal-to-noise ratio of the spectrometer, or more detected interference signals. Therefore, aμ and μ′s at 905–1600 nm were taken as the effective data in this study.The system in g et al. . The reselengths , and theaμ spectra of apple samples storage at 25 and 0 °C are set out in −1) being 10 times higher than that of the others. In addition, the aμ value peaked at1485 nm when the apple was stored at 25 °C after 10 days. There was no significant difference between day 0 and day 20, and aμ decreased significantly on the 30th, 40th and 50th days. At 0 °C, aμ at 1485 nm showed a similar trend with that at 25 °C, with the largest value on the 30th day during storage.The results obtained from the mean aμ values of apple flesh stored at 25 and 0 °C were within 0.04 and 1.88, and within 0.05 and 1.67 mm−1, respectively. Generally, the aμ values in this study were comparable to those reported in other research. In the study by Ma et al. [aμ values of apple samples were 0.00–0.50 mm−1, measured by HIS at 1000–1800 nm. The research of Saeys et al. reported the aμ values of Royal Gala (0.00–2.20 mm−1) based on IS at 350–2200 nm [aμ obtained using IS is much higher than that with HIS. This may be due to the different sample tissue state, different varieties of apples, different wavelength ranges as well as different measured method in the absorption property [The a et al. , the μa –2200 nm . An impoproperty .μ′s spectral was flatter and steadily decreased with increasing wavelength. In addition, the sμ’ spectral changed consistently as storage time progressed, which was in accordance with studies of He et al. [−1, which was smaller than μ′s of Royal Gala (1.00–1.50 mm−1) [μ′s was more than seven times the value of aμ, ranging from 0.80 to 1.40 mm−1 at 25 °C, and from 0.04 to 0.11 mm−1 at 0 °C. This result was also reported in Wei et al. [Compared with absorption coefficient, the reduced scattering coefficient displays different changing patterns in the spectral region of 905–1600 nm c,d. The e et al.  and Cen e et al. . The sca50 mm−1) . This ma50 mm−1) . Within i et al. . In the i et al. .p < 0.05), from 13.85% to 11.26% at 25 °C and from 13.97% to 12.26% at 0 °C.At 25 and 0 °C, SSC showed the trend of increasing before decreasing, and separately reached the maximum value on the 10th and 30th day . As show−1, respectively. In addition, the content of sucrose decreased from 11.52 to 7.44 g kg−1. At 0 °C, the TSS, fructose and glucose contents reached the highest value at a slower rate than at 25 °C, and reached the peak on the 30th day, with levels of 60.34, 31.95 and 18.14 g kg−1, respectively. On the 150th day, the TSS, fructose and glucose contents were the lowest, 47.62, 26.66 and 13.09 g kg−1, respectively. The content of sucrose did not change significantly during 60-day storage and reached the minimum value of 7.86 g kg−1 on the 150th day.Increasing with storage time, the contents of TSS, fructose, glucose and sucrose gradually declined, and reached the lowest value at the end of storage. At 25 °C, the contents of TSS, fructose and glucose reached the highest value on the 10th day. After 50 days storage, the contents of TSS, fructose and glucose were reduced to 50.72, 25.67 and 17.62 g kgp < 0.05) in r values of 0.973 at 25 °C and 0.963 at 0 °C. Other soluble sugars had lower contributions to the correlations with SSC, shown as a second relation with TSS and fructose, and finally glucose. The results were consistent with the report by Wei et al. [r > 0.847), and fructose contributed the most to the correlation with TSS, with r values of 0.974 at 25 °C and 0.975 at 0 °C. This could be expected as the content of fructose comprised the largest fraction of the TSS content.Relationships among the contents of TSS, fructose, glucose, sucrose and SSC were measured on a one to one basis, and SSC presented a positive association profile with TSS, fructose, glucose and sucrose during storage at 25 and 0 °C  and sucrose (r = 0.934) had the highest average correlation coefficients with aμ, followed by TSS (r = 0.928), and finally fructose (r = 0.915) and glucose (r = 0.899) at 25 ° C. The phenomena may be connected to the strong correlation between SSC and sucrose content. At 0 ° C, r values of aμ and the SSC, TSS content and soluble sugars contents ranged from 0.890 to 0.930. The result also showed that aμ peaked at 980, 1169 and 1485 nm, and the correlation at the three peaks increased successively, with r ranges of 0.881–0.922, 0.894–0.938 and 0.913–0.961, which was in good agreement with the successively stronger spectral signal. To further investigate the correlations with SSC and sucrose content, aμ at 980, 1169 and 1485 nm with SSC and sucrose content (25 °C). The coefficients of determination (R2) of the equations were between 0.848 and 0.921. Results from the equations indicate that SSC and sucrose content gave good linear correlations with aμ, with an R2 of 0.915 and 0.921, respectively, which confirms the results shown in Overall, SSC , followed by fructose (r = 0.599–0.816) and glucose (r = 0.544–0.738). The same result was found in the correlation of SSC and aμ with the three soluble sugars. The findings proved that the sucrose contributed the most to the strong relationship between optical properties and SSC.The values of r between velength c,d whichaμ was stronger than that between soluble sugars and μ′s in the whole visible-medium wave near infrared region. Besides, with the increase in wavelength, the correlation between soluble sugars and aμ increased, while the correlation between soluble sugars and μ′s decreased. It could be inferred that aμ was more associated with chemical components, while μ′s was less correlated with them [r of–0.68 between the μ′s and firmness. Liu et al. showed that μ′s of kiwifruit had stronger correlation with firmness (r = 0.74–0.87) than with SSC (r = 0.49–0.81) and moisture content (r = 0.59–0.80) [According to the results of ith them ,30,36,46ith them  measuredss r = 0.4–0.87 thaμ and μ′s at 905–1600 nm for SSC and the contents of soluble sugars of Fuji apple. The cR2 of the modeling set was higher than the pR2 of the prediction set, thus these models were reliable. Among the three prediction models of soluble sugars based on aμ and μ′s, the best model was for sucrose content . In addition, the models relied on aμ, and the μ′s for fructose and glucose content were slightly worse, the pR2 values of which were in the range of 0.635–0.781 and 0.593–0.725, and the RMSEP values were 1.570–2.216 g/kg and 1.344–1.906 g/kg. A previous study reported similar results in that the prediction results on optical properties for sucrose were better than fructose and glucose [pR2 values of the five prediction models based on aμ were in the range of 0.752–0.851, and the RMSEP values of the SSC and soluble sugar models were 0.329% and 1.047–2.062 g/kg, respectively. Li et al. [pR2 and RMSEP in the ranges of 0.576–0.854 and 0.547–0.673%, and the prediction accuracy of SSC was equivalent to the present study. In addition, according to the model results . The models for sucrose in 400–1050 nm built with the AIS system, according to the report of Wei et al. [pR2 and RMSEP values were 0.804 and 1.099, respectively. The result might be caused by the presence of the more obvious bonds C-H, O-H, C-C with a longer wavelength, which indicated that the absorption properties of the mid-wave near infrared region are more suitable for the quantitative detection of soluble solids and soluble sugars.In general, the prediction models for sucrose based on i et al. , were seaμ, μ′s, SSC and soluble sugars contents showed a similar pattern at the two experimented temperatures during storage. Three prominent absorption peaks at 980, 1169 and 1485 nm were noticed for the spectra of aμ. In addition, aμ and μ′s were positively associated with SSC and soluble sugars contents (r = 0.659–0.936), and their values are greatly relied on the wavelength. Thus, it could be easily understood that the decreases in SSC and soluble sugar content were accompanied by declines in aμ and μ′s. Additionally, the aμ had stronger correlation with these soluble components than with μ′s. Another important conclusion was that both SSC and aμ were more strongly correlated with sucrose content, with r values of 0.933, 0.934 (at 25 °C) and 0.930, 0.927 (at 0 °C). Moreover, partial least square regression (PLS) models for SSC and soluble sugars contents were developed, with the best performance of pR2 = 0.851 and RMSEP = 1.047, which appeared in the model of sucrose based on aμ. Furthermore, due to better measuring bonds of C-H, O-H, C-C at the longer wavelength range, the prediction model at 905–1600 nm performed better than the models developed in the 400–1050 nm range in the report of Wei et al. [The optical properties of Fuji apple during storage in two different storage conditions  were evaluated by the AIS system at 905–1600 nm, and the exact relationships of absorption and scattering properties with SSC and soluble sugars contents were explored. The changes of However, this study was carried out on a relatively limited number of fruit and only on apples from one cultivar. Further work needs to involve the investigation of exact relationships between absorption and scattering properties and soluble sugars contents of other varieties of apple flesh, as well as the correlation of optical properties with other components which contribute to quality, such as to firmness.