Evaluation of convolutional neural network for non-destructive detection of imidacloprid and acetamiprid residues in chili pepper (Capsicum frutescens L.) based on visible near-infrared spectroscopy.

Consumption of agricultural products with pesticide residue is risky and can negatively affect health. This study proposed a nondestructive method of detecting pesticide residues in chili pepper based on the combination of visible and near-infrared (VIS/NIR) spectroscopy (400-2498 nm) and deep learning modeling. The obtained spectra of chili peppers with two types of pesticide residues (acetamiprid and imidacloprid) were analyzed using a one-dimensional convolutional neural network (1D-CNN). Compared with the commonly used partial least squares regression model, the 1D-CNN approach yielded higher prediction accuracy, with a root mean square error of calibration of 0.23 and 0.28 mg/kg and a root mean square error of prediction of 0.55 and 0.49 mg/kg for the acetamiprid and imidacloprid data sets, respectively. Overall, the results indicate that the combination of the 1D-CNN model and VIS/NIR spectroscopy is a promising nondestructive method of identifying pesticide residues in chili pepper.

Visualization and (Semi-)quantification of submicrometer plastics through scanning electron microscopy and time-of-flight secondary ion mass spectrometry.

Increasing numbers of studies have demonstrated the existence of nanoplastics (1-999 nm) in the environment and commercial products, but the current technologies for detecting and quantifying nanoplastics are still developing. Herein, we present a combination of two techniques, e.g., scanning electron microscopy (SEM) and time-of-flight secondary ion mass spectrometry (ToF-SIMS), to analyze submicron-sized plastics. A drop-casting of a 20-nL particle suspension on a Piranha solution-cleaned silicon wafer with dry ice incubation and subsequent freeze-drying was used to suppress the coffee-ring effect. SEM images were used to quantify particles, and this technique is applicable for 0.195-1.04-µm polystyrene (PS), 0.311-µm polyethylene terephthalate (PET), and 0.344-µm polyethylene (PE) at a minimum concentration of 2.49 × 109 particles/mL. ToF-SIMS could not quantify the particle number, while it could semi-quantitatively estimate number ratios of submicron PE, PET, polyvinyl chloride (PVC), and PS particles in the mixture. Analysis of submicron plastics released from three hot water-steeped teabags (respectively made of PET/PE, polylactic acid (PLA), and PET) was revisited. The SEM-derived sizes and particle numbers were comparable to those measured by a nanoparticle tracking analysis (NTA) regardless of whether or not the hydro-soluble oligomers were removed. ToF-SIMS further confirmed the number ratios of different particles from a PET/PE composite teabag leachate. This method shows potential for application in analyzing more-complex plastic particles released from food contact materials.

Association Between Essential and Non-essential Metals, Body Composition, and Metabolic Syndrome in Adults.

Growing evidence indicates that metal exposure is associated with metabolic syndrome (MetS); however, mixed results have been reported. The aim of this study was to clarify associations of exposure to essential and non-essential metals with body composition and risks of obesity and MetS. Anthropometry and blood biochemistry of metabolic parameters were obtained from 150 middle-aged Taiwanese adults. Plasma metals were assessed using inductively coupled plasma mass spectrometry, and body compositions were measured by a bioelectrical impedance analysis (BIA). The essential metals of copper (Cu), manganese (Mn), and chromium (Cr) were positively correlated with the body fat mass but inversely correlated with the skeletal muscle mass (all p < 0.05). An adjusted logistic regression showed that Mn [odds ratio (OR) = 1.624 (95% confidence interval 1.072, 2.462), p = 0.02] and, to a lesser extent, Cu [OR = 1.501 (0.985, 2.292), p = 0.059] predicted abdominal obesity, while plasma Cu [OR = 2.211 (1.146, 4.266), p = 0.02] and zinc (Zn) [OR = 2.228 (1.048, 4.736) p = 0.04] predicted MetS. Significant correlations between dyslipidemia and lithium [OR = 1.716 (1.080, 2.726)], Cu [OR = 2.210 (1.415, 3.454)], Mn [OR = 2.200 (1.320, 3.666)], molybdenum [OR = 1.853 (1.160, 2.958)], and Zn [OR = 1.993 (1.186, 3.349)], and between boron [OR = 2.583 (1.137, 5.868)] and hyperglycemia were observed (all p < 0.05). Exposure to essential metals may affect the body composition and metabolic profiles, exacerbating the risk of MetS.

Adherence to COVID-19 nutritional guidelines and their impact on the clinical outcomes of hospitalized COVID-19 patients.

BACKGROUND & AIMS: Coronavirus disease 2019 (COVID-19) patients are at high risk of malnutrition, and their doctors are part of a multidisciplinary team, including nutritionists. However, adherence to nutritional guidelines may be difficult in the context of capacity constraints during the COVID-19 pandemic. The aim of this study was to investigate barriers to doctors' adherence to nutritional guidelines and the impacts of guideline adherence on the outcomes of hospitalized COVID-19 patients. METHODS: A multinational electronic survey involving 51 doctors was conducted between November 2020 and January 2021 from 17 COVID-19-designated hospitals in countries with high (Indonesia) and low (Vietnam) numbers of confirmed COVID-19 cases. RESULTS: In general, doctors reported concerns related to nutritional practices in patients with Covid-19 which included feeling stress when performing medical nutritional therapy (65%), lacking self-efficacy or confidence in performing nutritional care (49%), lacking clear nutritional guidelines (45%), and experiencing budget limitations (33%). A regression analysis adjusted for age, country, and the number of hospitalized COVID-19 cases revealed that guideline knowledge (ß: -1.01 (-1.78, -0.23); p = 0.012) and awareness of guidelines (ß: -1.37 (-2.66, -0.09); p = 0.037) were negatively correlated with the length of stay of critically ill COVID-19 patients, but non-significant after adjusting for specialization of the doctor. When stratified according to country, a significant relationship between guideline adherence and length of stay of critically ill patients was only found in Vietnam [guideline adherence: ß: -0.55 (-1.08, -0.03); p = 0.038; guideline knowledge: ß: -1.01 (-1.9, -0.13); p = 0.027] after adjusting for age, specialty, and number of hospitalized COVID-19 cases. In Indonesia, the significant relationship between guideline adherence and mortality of COVID-19 patients remained strong (ß: -14 (-27, -1); p = 0.033) after adjusting for age, specialty, and number of hospitalized COVID-19 cases CONCLUSIONS: Inadequate nutritional knowledge is a key barrier to guideline adherence, and this was international and may be related to doctors' specialties and the COVID-19 pandemic. Adherence to nutritional guidelines may represent a prognostic factor for survival in COVID-19 patients.

Application of OXITEST for Prediction of Shelf-Lives of Selected Cold-Pressed Oils.

Introduction: Due to the enhanced awareness of consumers concerning healthy foods, homemade expeller-pressed oils have become popular worldwide. However, an extended storage period may lead to oxidization of the oil and exposure to hazardous byproducts by consumers. Methods: In this study, 10 pressed oil samples prepared from common oilseeds using a small-scale expeller oil press were analyzed by OXITEST with a sample amount of 5 g of oil and an oxygen pressure of 800 kPa under accelerated conditions for shelf-life projections. The oil properties were investigated, including the recovery, smoke point, acid value, iodine value, "fatty acid composition, and contents of pigments and tocopherols". Results: The autoxidation reaction of various expeller-pressed oils under an accelerated testing system followed zero-order Arrhenius kinetics (R 2 > 0.99). Shelf-lives of the pressed oils at 25°C were estimated by extrapolation to range 105~1,089 days. The obtained shelf-lives were significantly correlated with log induction period (IP) values (r > 0.81, p < 0.05) and unsaturated fatty acids (UFAs) (r < -0.69, p < 0.05), but not with the iodine value, acid value, or smoke point. Scatter diagrams between shelf-lives and UFAs suggested that these pressed oils could be grouped by two linear regression curves (r > 0.98, p < 0.05). The predictive equations using multiple linear regression are presented herein, with predictor variables of UFAs and an unspecified item involving potential influencing factors such as tocopherol contents (r > 0.88, p < 0.05). Conclusions: Our findings first revealed that the UFA portion was partially correlated with the shelf-lives of selected expeller-pressed seed oils as estimated by the OXITEST. The derived equations can be applied for shelf-life predictions of expeller-pressed oils stored under dark ambient conditions based on the fatty acid profile.

High-dose ferric citrate supplementation attenuates omega-3 polyunsaturated fatty acid biosynthesis via downregulating delta 5 and 6 desaturases in rats with high-fat diet-induced obesity.

Obesity is associated with an increased risk of an iron deficiency; however, a synergistic relationship between iron and lipid homeostasis was also observed. The aim of this study was to investigate the effects of pharmacological doses of iron supplementation on omega 3 (n-3) and omega 6 (n-6) polyunsaturated fatty acids (PUFAs). Sprague-Dawley (SD) rats were fed a normal diet or a 50% high-fat diet (HFD) without or with pharmacological doses of ferric citrate (0.25, 1, or 2 g ferric iron per kg diet) for 12 weeks, and erythrocyte profiles of n-3 and n-6 PUFAs were quantitated. Ferric citrate supplementation showed dose-related effects on liver inflammation, liver iron accumulation, and increasing circulating levels of iron, erythrocyte degradation biomarkers LVV-hemorphin-7, malondialdehyde (MDA), and insulin. Obese rats supplemented with 2 g ferric iron per kg diet also had decreased levels of eicosapentaenoic acid (EPA), docosapentaenoic acid (DPA), and total n-3 PUFAs compared to rats fed a normal diet or HFD alone. A western blotting analysis revealed that iron-mediated downregulation of n-3 PUFA-converting enzymes (Δ5 and Δ6 desaturases) only occurred at high dosages (≥1 g ferric iron per kg diet). A Spearman correlation analysis showed that total liver iron and serum LVV-hemorphin-7 and MDA were negatively correlated with n-3 PUFAs and their converting enzymes (Δ5 and Δ6 desaturases) (all p < 0.05). In conclusion, obese rats that received high-dose ferric citrate supplementation (>1 g of ferric iron per kg diet) exhibited decreased n-3 PUFA levels via downregulation of expressions of Δ5 and Δ6 desaturase enzymes.

Synergistic Interaction of Dietary Pattern and Concordance Lifestyle with Abnormal Liver Function among Young Adults in Taiwan: A Population-Based Longitudinal Study.

While diet and lifestyle are independently implicated in the etiology of liver disease, the interaction of diet and lifestyle may be more helpful for determining the risk of liver abnormality. Thus, our study aimed to evaluate the interaction between the dietary pattern associated with liver biomarkers and lifestyle factors among Taiwanese adults with abnormal liver enzymes. A liver-associated dietary pattern, generated using reduced rank regression, was characterized by high intake of soy sauce or other dips, sugar sweetened beverages, and preserved and processed foods, but low intake of seafood, fruits, eggs, and dark-colored vegetables. In the fully adjusted model, liver-associated dietary patterns or unhealthy concordance lifestyle factors were associated with an increased risk of having liver function abnormality (OR = 1.08, 95% CI: 1.04, 1.12 and OR = 1.42, 95% CI: 1.31, 1.53, respectively). Moreover, the interaction between liver-associated dietary pattern and unhealthy concordance lifestyle factors showed more significant correlation, with an elevated risk of abnormal liver function (OR = 2.14, 95% CI: 2.02, 2.26). Therefore, our study suggests that participants who have a strong liver-associated dietary pattern along with unhealthy concordance lifestyles are likely to have increased odds of abnormal liver function.

Clinical Mass Spectrometry Discovered Human IgG Sialylation as a Potential Biosignature for Kidney Function.

Immunoglobulin G (IgG) N-glycosylation was discovered to have an association with inflammation status, which has the potential to be a novel biomarker for kidney diseases. In this study, we applied an ultra-high performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) method to plasma and urine samples from 57 individuals with different levels of kidney function. Natural abundances of total IgG, IgG1, IgG2, and IgG3 subclasses in plasma showed positive correlations to the estimated glomerular filtration rates (eGFRs). Eighteen IgG glycopeptides also showed positive correlations. In contrast, higher IgG amounts were found in urine samples from participants with lower eGFR values. After normalizing IgG glycopeptides from plasma to their respective protein amounts, H4N4F1S1-IgG1 (r = 0.37, p = 0.0047, significant) and H5N4F1S1-IgG1 (r = 0.25, p = 0.063, marginally significant) were the two glycopeptides that still had positive correlations with eGFRs. The results showed that the UHPLC-MS/MS method is capable of investigating IgG profiles, and monitoring IgG and glycosylation patterns is worthy of further clinical application for kidney disease.

Prediction of tea theanine content using near-infrared spectroscopy and flower pollination algorithm.

In this study, near-infrared (NIR) spectroscopy was exploited for non-destructive determination of theanine content of oolong tea. The NIR spectral data (400-2500 nm) were correlated with the theanine level of 161 tea samples using partial least squares regression (PLSR) with different wavelengths selection methods, including the regression coefficient-based selection, uninformative variable elimination, variable importance in projection, selectivity ratio and flower pollination algorithm (FPA). The potential of using the FPA to select the discriminative wavelengths for PLSR was examined for the first time. The analysis showed that the PLSR with FPA method achieved better predictive results than the PLSR with full spectrum (PLSR-full). The developed simplified model using on FPA based on 12 latent variables and 89 selected wavelengths produced R-squared (R2) value and root mean squared error (RMSE) of 0.9542, 0.8794 and 0.2045, 0.3219 for calibration and prediction, respectively. For PLSR-full, the R2 values of 0.9068, 0.8412 and RMSEs of 0.2916, 0.3693, were achieved for calibration and prediction. Also, the optimized model using FPA outperformed other wavelengths selection methods considered in this study. The obtained results indicated the feasibility of FPA to improve the predictability of the PLSR and reduce the model complexity. The nonlinear regression models of support vector machine regression and Gaussian process regression (GPR) were further utilized to evaluate the superiority of using the FPA in the wavelength selection. The results demonstrated that utilizing the wavelength selection method of FPA and nonlinear regression model of GPR could improve the predictive performance.

Quantitative Analysis and Discrimination of Partially Fermented Teas from Different Origins Using Visible/Near-Infrared Spectroscopy Coupled with Chemometrics.

Partially fermented tea such as oolong tea is a popular drink worldwide. Preventing fraud in partially fermented tea has become imperative to protect producers and consumers from possible economic losses. Visible/near-infrared (VIS/NIR) spectroscopy integrated with stepwise multiple linear regression (SMLR) and support vector machine (SVM) methods were used for origin discrimination of partially fermented tea from Vietnam, China, and different production areas in Taiwan using the full visible NIR wavelength range (400-2498 nm). The SMLR and SVM models achieved satisfactory results. Models using data from chemical constituents' specific wavelength ranges exhibited a high correlation with the spectra of teas, and the SMLR analyses improved discrimination of the types and origins when performing SVM analyses. The SVM models' identification accuracies regarding different production areas in Taiwan were effectively enhanced using a combination of the data within specific wavelength ranges of several constituents. The accuracy rates were 100% for the discrimination of types, origins, and production areas of tea in the calibration and prediction sets using the optimal SVM models integrated with the specific wavelength ranges of the constituents in tea. NIR could be an effective tool for rapid, nondestructive, and accurate inspection of types, origins, and production areas of teas.

Biomolecular Clusters Distribution up to Mega Dalton Region Using MALDI-Quadrupole Ion Trap Mass Spectrometer.

We present the first report on complete cluster distributions of cytochrome c (molecular weight of 12.4 kDa) and bovine serum albumin ((BSA), molecular weight of 66.4 kDa) with mass-to-charge ratio (m/z) reaching 350,000 and 1,400,000, respectively, by matrix-assisted laser desorption/ionization (MALDI). Large cluster distributions of the analytes were measured by our homemade frequency-scanned quadrupole ion trap (QIT) mass spectrometer with a charge detector. To our knowledge, we report the highest m/z clusters of these two biomolecules. The quantitative results indicate that large clusters ions of cytochrome c and BSA follow the power law (r² > 0.99) with cluster size distribution, which provides experimental evidence for the laser ablation studies of MALDI.

Integration of independent component analysis with near-infrared spectroscopy for analysis of bioactive components in the medicinal plant Gentiana scabra Bunge.

Independent component (IC) analysis was applied to near-infrared spectroscopy for analysis of gentiopicroside and swertiamarin; the two bioactive components of Gentiana scabra Bunge. ICs that are highly correlated with the two bioactive components were selected for the analysis of tissue cultures, shoots and roots, which were found to distribute in three different positions within the domain [two-dimensional (2D) and 3D] constructed by the ICs. This setup could be used for quantitative determination of respective contents of gentiopicroside and swertiamarin within the plants. For gentiopicroside, the spectral calibration model based on the second derivative spectra produced the best effect in the wavelength ranges of 600-700 nm, 1600-1700 nm, and 2000-2300 nm (correlation coefficient of calibration = 0.847, standard error of calibration = 0.865%, and standard error of validation = 0.909%). For swertiamarin, a spectral calibration model based on the first derivative spectra produced the best effect in the wavelength ranges of 600-800 nm and 2200-2300 nm (correlation coefficient of calibration = 0.948, standard error of calibration = 0.168%, and standard error of validation = 0.216%). Both models showed a satisfactory predictability. This study successfully established qualitative and quantitative correlations for gentiopicroside and swertiamarin with near-infrared spectra, enabling rapid and accurate inspection on the bioactive components of G. scabra Bunge at different growth stages.