Quantitative NIR determination of isoflavone and saponin content of ground soybeans

Outlier Removal with Weight Penalization and Aggregation: A Robust Variable Selection Method for Enhancing Near-Infrared Spectral Analysis Performance

Full-wavelength near-infrared (NIR) spectroscopy faces significant challenges due to the strong collinearity among spectral variables and the presence of variables that are highly sensitive to sample fluctuations. Additionally, not all spectral variables contribute equally to the NIR model. Weakly influential variables, although not important on their own, can provide substantial improvement when combined with stronger variables, thus increasing both model stability and prediction accuracy. Therefore, this study proposes a new variable selection method called outlier removal with weight penalization and aggregation (OR-WPA). The method begins by removing outlier spectral variables with high coefficient of variation, which enhances model stability. During the variable selection process, multiple submodels are constructed based on variable subsets, with variable weights assigned according to the absolute values of regression coefficients. A moving window is applied to average the weights, and variables with excessively high weights are penalized, promoting the selection of weakly influential variables that positively contribute to model accuracy. The variable space is iteratively reduced, and the subset of variables associated with the highest predictive accuracy is selected as the final characteristic variable combination. The OR-WPA method was evaluated on three NIR spectral data sets, involving corn, heated tobacco substrate, and flue-cured tobacco. The results were compared with three advanced variable selection methods: Monte Carlo uninformative variable elimination, competitive adaptive reweighted sampling, and bootstrapping soft shrinkage. The results indicate that OR-WPA demonstrates better predictive performance, particularly in predicting low-content components, where it significantly enhances both the accuracy and stability of the NIR model.

A Comprehensive Review of Antinutrients in Plant-Based Foods and Their Key Ingredients

In recent years, the growing popularity of vegan and vegetarian diets, along with the rising demand for plant-based foods, has led researchers to concentrate on examining the presence and effects of antinutrients. While there are existing literature reviews focusing on antinutritional compounds, particularly on their reduction, this review aims to provide a comprehensive description of antinutrients for producers, food scientists, professionals, legislators and consumers, emphasising the complexity of the subject and the necessity for diverse approaches while identifying aspects that still require further research. Antinutrients, including protease inhibitors, amylase inhibitors, phytic acid, lectins, saponins, tannins, cyanogenic glycosides, oxalic acid, polyphenols, goitrogens and pyrimidine glycosides, are described, encompassing various aspects such as their structural characteristics, analytical detection methods, distribution, physiological impacts and strategies for mitigation or elimination. Specifically, our review concentrates on assessing the presence of antinutrients in plant-based food products and the primary ingredients, categorised into five distinct groups, cereals, pseudocereals, pulses, seeds and nuts, which are commonly used in their production. Among these categories, legumes are identified as the richest source of anti-nutritional compounds, followed by cereal grains. However, certain pseudocereals, seeds and nuts also demonstrate high levels of specific antinutrients. While antinutrients are generally regarded as harmful to nutrient absorption, recent studies have revealed some potential health benefits associated with them. Therefore, further in vivo research is essential to elucidate the behaviour of antinutritional compounds within the human body. Additionally, there is a significant lack of formal regulations and guidelines regarding antinutrients, and food products currently do not feature labelling related to these compounds.

The impact of milling degree on physicochemical, nutritional, rheological, textural properties, and in vitro digestibility of rice: From brown rice to white rice

The objective of this study was to characterize the rice milling fractions acquired at each stage of a commercial milling system. This characterization included an analysis of color, ash content, dietary fiber, mineral composition, as well as antinutritional compounds like phytic acid, trypsin inhibitor activity, and saponin. Additionally, we investigated in vitro starch and in vitro protein digestibility, along with pasting, cooking, and textural properties. Our findings revealed that milling improved the visual appeal of rice (e.g., volume expansion, weight gain, whiteness) and notably enhanced its starch digestibility. However, milling reduced dietary fiber, mineral content, antinutrients, cooking time, and texture characteristics such as hardness, chewiness, gumminess, springiness, and cohesiveness to varying degrees. Certain parameters, such as dietary fiber, exhibited a gradual change with the duration of milling, while others, such as mineral content and texture, showed significant variation at the initial stage of milling, particularly at the first mill.

Fermented soy products: A review of bioactives for health from fermentation to functionality

The increasing prevalence of metabolic diseases and the global drive toward achieving Sustainable Development Goals (SDGs) underscore the need for sustainable, nutrient-dense foods. Soybeans (Glycine max), a critical global crop, offer promising solutions; however, their predominant use as animal feed raises concerns regarding food security and environmental sustainability. Fermented soy products-including tempeh, natto, and miso-are rich in bioactive compounds such as peptides and isoflavones, which offer potential therapeutic effects and hold cultural and nutritional significance. These fermented products provide bioactive profiles with unique health-promoting properties. This review critically examines the bioactive compounds generated through fermentation, focusing on their bioconversion pathways in the gastrointestinal tract and their metabolic implications for human health. Recent consumer demand for novel food ingredients with additional biological benefits has fueled research into advanced extraction techniques, enhancing the functional applications of bioactive compounds from these soy-based products. This review further explores innovations in extraction methods that improve bioactive yield and sustainability, reinforcing the applicability of these compounds in health-promoting food interventions. The originality of this review lies in its in-depth exploration of the gastrointestinal bioconversion of fermented soy bioactive compounds alongside the latest sustainable extraction methods designed to optimize their use. Future research should aim to refine fermentation and extraction processes, investigate synergistic microbial interactions, and develop environmentally sustainable production methods. These efforts have the potential to position fermented soy products as essential contributors to global nutritional security and sustainable food systems, addressing both public health and environmental needs.

The Role of Near-Infrared Spectroscopy in Food Quality Assurance: A Review of the Past Two Decades

During food quality control, NIR technology enables the rapid and non-destructive determination of the typical quality characteristics of food categories, their origin, and the detection of potential counterfeits. Over the past 20 years, the NIR results for a variety of food groups-including meat and meat products, milk and milk products, baked goods, pasta, honey, vegetables, fruits, and luxury items like coffee, tea, and chocolate-have been compiled. This review aims to give a broad overview of the NIRS processes that have been used thus far to assist researchers employing non-destructive techniques in comparing their findings with earlier data and determining new research directions.

Holistic quality evaluation method of Epimedii Folium based on NIR spectroscopy and chemometrics

INTRODUCTION

There are some problems in the quality control of Epimedii Folium (leaves of Epimedium brevicornum Maxim.), such as the mixed use of Epimedii Folium from different harvesting periods and regions, incomplete quality evaluation, and time-consuming analysis methods.

OBJECTIVE

Near-infrared (NIR) spectroscopy was conducted to establish a rapid overall quality evaluation method for Epimedii Folium.

MATERIALS AND METHODS

Quantitative models of the total solid, moisture, total flavonoid, and flavonol glycoside (Epimedin A, Epimedin B, Epimedin C, Icariin) contents of Epimedii Folium were established by partial least squares regression (PLSR). The root mean square error (RMSE) and correlation coefficient (R) were used to evaluate the performance of models. The qualitative models of Epimedii Folium from different geographic origins and harvest periods were established based on K-nearest neighbor (KNN), back-propagation neural network (BPNN), and random forest (RF). Accuracy and Kappa values were used to evaluate the performance of models. A new multivariable signal conversion strategy was proposed, which combines NIR spectroscopy with the PLSR model to predict the absorbance values of retention time points in the high-performance liquid chromatography (HPLC) fingerprint to obtain the predicted HPLC fingerprint. The Pearson correlation coefficient and cosine coefficient were used to evaluate the similarity between real and predicted HPLC fingerprints.

RESULTS

Qualitative models, quantitative models, and the similarity between real and predicted HPLC fingerprints are satisfactory.

CONCLUSION

The method serves as a fast and green analytical quality evaluation method of Epimedii Folium and can replace traditional methods to achieve the overall quality evaluation of Epimedii Folium.

Effects of Daidzein, Tempeh, and a Probiotic Digested in an Artificial Gastrointestinal Tract on Calcium Deposition in Human Osteoblast-like Saos-2 Cells

Adequate calcium intake is crucial for the prevention and treatment of bone-related issues. Developing a nutritional source of readily bioavailable calcium is particularly significant for individuals deficient in this essential element and at risk of developing osteoporosis. This research aimed to evaluate the impact of tempeh (T), daidzein (D), and Lactobacillus acidophilus (LA) within a simulated intestinal environment consisting of Caco-2 epithelial and Saos-2 cells, focusing on their implications for bone mineralization mechanisms. In the initial phase, calcium bioaccessibility from calcium citrate (CaCt), LA, D, the daidzein combination D-CaCt-LA (D1:1:1), and the tempeh combination T-CaCt-LA (T1:1:1) was assessed through digestion simulation. The calcium content of both untreated and digested samples was determined using atomic absorption spectrometry (AAS). In the subsequent stage, the digested samples were used to induce intestinal absorption in differentiated enterocyte-like Caco-2 cells. The permeable fractions were then evaluated in a culture of osteoblast-like Saos-2 cells. Preliminary cellular experiments employed the MTT assay to assess cytotoxicity. The results indicated that the analyzed products did not influence the deposition of extracellular calcium in Saos-2 cells cultured without mineralization stimulators. The combined formulations of permeable fractions of digested CaCt, LA, D, and T demonstrated the capacity to enhance the proliferation of Saos-2 cells. In Saos-2 cells, D, D1:1:1, and LA showed no discernible impact on intracellular calcium accumulation, whereas T and T1:1:1 reduced the calcium deposits. Additionally, mRNA transcripts and alkaline phosphatase (ALP) activity levels in Saos-2 cells cultured without mineralization induction were unaffected by the analyzed products. An examination of the products revealed no discernible effect on ALP activity or mRNA expression during Saos-2 cell differentiation. Our findings suggest that tempeh, daidzein, and L. acidophilus did not positively impact cellular calcium deposition in Saos-2 cells. However, tempeh, daidzein and its combination, and L. acidophilus might enhance the process of osteogenic differentiation in Saos-2 cells. Nevertheless, this study did not identify any synergistic impact on calcium deposition and the process of osteogenic differentiation in Saos-2 cells of isoflavones and probiotics.

Discriminating geographical origins and determining active substances of water caltrop shells through near-infrared spectroscopy and chemometrics

Near-infrared spectroscopy (NIRS) combined with chemometric methods were used to discriminate the geographical origins of the water caltrop shells from different regions of China. Two active substances, the total phenolic content (TPC) and total flavonoid content (TFC) in the water caltrop shells were determined through the technique as well. Principal component analysis (PCA) combined with linear discriminant analysis (LDA) was adopted to build the geographical discriminant model. Quantitative analysis models of TPC and TFC were built using partial least squares (PLS) regression. 1st derivative and randomization test (RT) methods were used to optimize the quantitative analysis models. It was found that the geographical discriminant model can correctly recognize the water caltrop shells from different regions of China with a total accuracy of 93.33%. The values of TPC and TFC obtained by the optimized models and the standard method are close. The coefficient of determination (R2) and the ratio of prediction to deviation for the two substances were 0.91, 0.89 and 3.02, 3.02, respectively. The results demonstrated the feasibility of NIRS combined with chemometric methods for the geographical discrimination of water caltrop shells and the quantitative analysis of TPC and TFC in water caltrop shells.

Nutritive value of fermented soybean grains for ruminants

Fermented soybean grain (FSBG) is considered improper to use as a protein source in animal nutrition, since it is assumed that defects cause changes on its chemical composition and favor mycotoxins production, but chemical composition data does not support this theory and in vivo studies are missing. Thus, this study aimed to evaluate the effects of FSBG in feedlot lamb diets. For that, two types of FSBG (partially fermented and completely fermented, PFSBG and CFSBG) and one standard soybean grain (SSBG) were obtained and evaluated alone or as a component of experimental diets by in vitro and in vivo studies, where FSBG totally replaced SSBG in feedlot lamb diets, which was included in the experimental diets in 17.4% on dry matter basis as protein source. Before the studies, both soybeans were sent to a specialized laboratory where no mycotoxins were detected. As a result, lower DM and carbohydrate contents but higher crude protein, fiber, and indigestible NDF contents were measured in CFSBG than in SSBG. Furthermore, both types of FSBG showed lower digestibility in vitro dry matter (IVDMD) than SSBG when evaluated separately; however, when evaluated in experimental diets, the substitution of SSBG for FSBG did not affect IVDMD. It was also observed that FSBG also had less rumen-degradable protein than SSBG (mean 47.9 vs 86.4%). In the in vivo study, FSBG did not affect nutrient intake, apparent digestibility, or animal performance (i.e., average daily gain and carcass gain). Thus, mycotoxins-free FSBG may be an alternative to totally replace SSBG in feedlot lamb diets.

Effects of isoflavone and probiotic intake on calcium transport and bone metabolism biomarkers in female rats

Calcium is essential for maintaining bone health as it contributes to bone formation, remodeling, strength, and density. This study investigated the effect of isoflavones and probiotics on calcium transporters' gene expression, serum calcium levels, and bone metabolism biomarkers in healthy female rats. Forty-eight female Wistar rats were classified into six groups. Bone metabolism biomarkers (pyridinoline, deoxypyridinoline, parathyroid hormone, and osteocalcin) and serum calcium levels were measured by enzyme-linked immunosorbent assay (ELISA) and atomic absorption spectroscopy (AAS), respectively. Gene expression of calcium transporters (Trpv5 and Trpv6) was evaluated in duodenum and jejunum tissue samples using quantitative polymerase chain reaction (qPCR). Trpv5 and Trpv6, epithelial calcium channels, play a crucial role in calcium transport and homeostasis in the body. The study consisted of a1-week adaptation period for the rats to adjust to the controlled conditions, followed by an 8-week intervention phase. The daidzein and genistein group showed a significant increase in the gene expression of the Trpv6 transporter in the duodenum and a marked decrease in serum pyridinoline levels compared to the control group. The tempeh and soybean groups showed a significant decrease in the gene expression of the Trpv5 calcium transporter in the jejunum. However, no significant influence of the Lactobacillus acidophilus diet on calcium transport and bone metabolism biomarkers was observed in the L. acidophilus group. The correlation analysis showed a significant positive relationship between serum calcium, bone metabolism biomarkers, and calcium transporters. In conclusion, our study demonstrates that the daidzein and genistein diet improves calcium transport in the duodenum and reduces pyridinoline serum concentrations, while tempeh and soybean diets reduce calcium transport in the jejunum. However, the combination of daidzein, genistein, and L. acidophilus did not demonstrate a synergistic effect on calcium transport and bone metabolism, suggesting that further investigations are needed to elucidate their potential interactions.

Development of Extraction Method for Determination of Saponins in Soybean-Based Yoghurt Alternatives: Effect of Sample pH

The number of plant-based dairy alternative products on the market is growing rapidly. In the case of soybean-based yoghurt alternatives, it is important to trace the content of saponins, the phytomicronutrients with a disputable health effect, which are likely to be responsible for the bitter off-taste of the products. We present a new sample extraction method followed by hydrophilic interaction liquid chromatography with mass spectrometric detection (HILIC-MS) for identifying and quantifying soyasaponins in soybean-based yoghurt alternatives. Soyasaponin Bb, soyasaponin Ba, soyasaponin Aa, and soyasaponin Ab were quantified using commercially available standard compounds and with asperosaponin VI as the internal standard. As the recoveries of soyasaponins were unacceptable in yoghurt alternatives at their natural acidic pH, the adjustment of pH was performed as one of the first steps in the sample extraction procedure to achieve the optimum solubility of soyasaponins. The validation of the method included the assessment of linearity, precision, limit of detection and limit of quantification (LOQ), recovery, and matrix effect. The average concentrations of soyasaponin Bb, soyasaponin Ba, soyasaponin Ab, and soyasaponin Aa in several measured soybean-based yoghurt alternatives utilising the developed method were 12.6 ± 1.2, 3.2 ± 0.7, 6.0 ± 2.4 mg/100 g, and below the LOQ, respectively. This method provides an efficient and relatively simple procedure for extracting soyasaponins from yoghurt alternatives followed by rapid quantification using HILIC-MS and could find a rightful application in the development of healthier and better-tasting dairy alternatives.

Variations in the morphological and chemical composition of the rhizomes of Polygonatum species based on a common garden experiment

Polygonatum species have great potential in fighting chronic and hidden hunger. In this study, five Polygonatum species collected from different populations were cultivated in a common garden for 4 years. The species mainly differed in yield, saponin and polysaccharide contents, stem diameter, leaf width, inflorescence length, and floret inflorescence length. P. cyrtonema (PC) provides high-quality yield when planted in Zhejiang, with output as high as 7.5 tons per hectare and a promising breeding potential. Moreover, stem diameter can be used as an indicator of the harvest in the screening of varieties. In addition, the formation of plant genetic traits from different provenances is affected by the climatic factors of the origin. Furthermore, near-infrared spectroscopy combined with chemometrics for polysaccharide and saponin quantitation provides a rapid assessment of PC quality. Our findings provide a scientific basis for the development and sustainable utilization of PC as a high-yielding and high-quality forest crop.

Classification for GM and Non-GM Maize Kernels Based on NIR Spectra and Deep Learning

The classification of GM and non-GM maize kernels is fundamental for further analysis of the gene action in maize. Therefore, a complete and novel detection scheme based on near-infrared spectra was designed to distinguish GM and non-GM maize kernels. Hyperspectral images (935–1720 nm) of 777 maize kernels from 3 kinds were captured, and the average spectra of the maize kernels were extracted for modeling analysis. The classical modeling methods based on feature engineering were first studied, and the backpropagation neural network–genetic algorithm model showed the best performance with a prediction accuracy of 0.861. Then, novel modeling methods based on deep learning were developed. To dig out the interactive information between different bands and match the application scenarios, the original spectra were transformed into two-dimensional matrices before establishing the deep learning models. A modified convolution neural network (i.e., VGG net) with dilated convolution was finally constructed to classify the maize kernels, and the prediction accuracy reached 0.961. This research provides a referential and novel way to detect GM maize kernels. Future research will improve the detection scheme for monitoring unauthorized GM organisms by introducing the visualization technology of deep learning.

Evaluation of Polysaccharide Content in Shiitake Culinary-Medicinal Mushroom, Lentinula edodes (Agaricomycetes), via Near-Infrared Spectroscopy Integrated with Deep Learning

Polysaccharide is one of the bioactive ingredients extracted from the fruiting body of Lentinula edodes (=L. edodes), which has many medicinal functions. While the content of polysaccharide can be measured by near-infrared (NIR) spectroscopy, the NIR analytical models established previously only covered L. edodes from very limited sources, and thus could not achieve high accuracy for large samples from more varied sources. Strictly, there is a nonlinear relationship between NIR spectral data and chemical label values, and traditional modeling methods for NIR data analysis have problems such as insufficient feature learning ability and difficulty in training. The deep learning model has excellent nonlinear modeling ability and generalization capacity, which is very suitable for analyzing larger samples. In this study, we constructed a novel framework with deep learning techniques on the NIR analysis of the content of polysaccharide in L. edodes. The siPLS model was established based on the combination of the bands 4797-3995 cm-1 and 6401-5600 cm-1, while the one-dimensional convolutional neural network (1D-CNN) model was established with improved feature in the treatment of the spectral data. The comparative experimental results showed that the 1D-CNN model (R2pre = 95.50%; RMSEP =0.1875) outperformed the siPLS model (R2pre = 87.89%, RMSEP = 0.6221). As such, this work has demonstrated that NIR spectroscopy with the integration of deep learning can provide more accurate quantification of polysaccharide in L. edodes. Such method can be very useful for nutritional grading and quality control of diverse L. edodes in the market.

Rapid Determination of Pachymic Acid Content by Near-Infrared Spectroscopy

In this paper, a rapid model for the determination of pachymic acid content in Poria was established by partial least squares (PLS) regression and near-infrared spectroscopy (NIR). During the research, a total of 108 batches of Poria samples from different producing regions were used, while their corresponding pachymic acid contents by high-performance liquid chromatography (HPLC) were adopted as reference. These samples were divided randomly into calibration sets for model establishment and validation sets for model validation. The test results from the calibration set showed that the best preprocessing method of the NIR spectra model was the standard normal variate (SNV) + second derivatives (SD), and the most suitable number of principal factors was 9. In this model, the coefficient of determination of the calibration set ( $r_c^2$ ) and validation set ( $r_v^2$ ) was 0.915 and 0.917, respectively. Meanwhile, the root mean square error of calibration (RMSEC) and the root mean square error of validation (RMSEP) with the calibration set were 0.051 mg/g and 0.054 mg/g, respectively. These results indicated this model could rapidly and reliably predict the pachymic acid content in Poria and increase the determination efficiency of pachymic acid in Poria. This is conducive to promote the development of industry.

Air-classified faba bean protein fraction as a substitute to soybean meal in pelleted and extruded broiler diets

1. The hypothesis that air-classified faba bean protein fraction (FBP) can replace soybean meal (SBM) in pelleted or extruded broiler diets without adverse effect on performance or nutrient digestibility was tested.2. At 17 d of age, male broilers were randomly distriibuted among four dietary treatments consisting of either SBM or FBP (main dietary protein source) and pelleting or extrusion as processing methods. Treatments had 10 replicate pens containing five birds each.3. Compared to SBM, birds fed FBP had significantly lower feed intake, less weight gain and had poorer feed conversion.4. Pellet durability was high (above 92%) for all diets. In pelleted diets, FBP was harder than SBM whereas extruded diets had similar hardness. Pelleting increased water stability compared to extrusion. FBP diets were more water stable than the SBM diets.5. Gizzard content weight was 2.2-fold higher (P = 0.002) for birds given FBP compared to those fed SBM. The weight of the jejunum and ileum with contents was 1.4-fold lower for the FBP diets, and this effect was larger (P < 0.05) for the extruded FBP diet.6. Birds fed FBP diets had significantly higher nitrogen digestibility in the lower jejunum and ileum compared to those given SBM diets, while the starch digestibility coefficient was above 0.980 in all treatments.7. The high nutrient digestibility of FBP diets indicates that the poor performance of the FBP group was due to lower feed intake which was not explained by the differences in pellet durability or hardness. The reduced palatability of the FBP and the longer retention of the FBP diets in the upper gut are more likely to have depressed feed intake.

Nondestructive Prediction of Isoflavones and Oligosaccharides in Intact Soybean Seed Using Fourier Transform Near-Infrared (FT-NIR) and Fourier Transform Infrared (FT-IR) Spectroscopic Techniques

The demand for rapid and nondestructive methods to determine chemical components in food and agricultural products is proliferating due to being beneficial for screening food quality. This research investigates the feasibility of Fourier transform near-infrared (FT-NIR) and Fourier transform infrared spectroscopy (FT-IR) to predict total as well as an individual type of isoflavones and oligosaccharides using intact soybean samples. A partial least square regression method was performed to develop models based on the spectral data of 310 soybean samples, which were synchronized to the reference values evaluated using a conventional assay. Furthermore, the obtained models were tested using soybean varieties not initially involved in the model construction. As a result, the best prediction models of FT-NIR were allowed to predict total isoflavones and oligosaccharides using intact seeds with acceptable performance (R²_p: 0.80 and 0.72), which were slightly better than the model obtained based on FT-IR data (R²_p: 0.73 and 0.70). The results also demonstrate the possibility of using FT-NIR to predict individual types of evaluated components, denoted by acceptable performance values of prediction model (R²_p) of over 0.70. In addition, the result of the testing model proved the model’s performance by obtaining a similar R² and error to the calibration model.

Prediction of protein and oil contents in soybeans using fluorescence excitation emission matrix

To investigate the potential of fluorescence spectroscopy in evaluating soybean protein and oil content, excitation emission matrix (EEM) was measured on 34 samples of soybean flours using a front-face measurement, and the accuracy of the protein and oil content prediction was evaluated. The EEM showed four main peaks at excitation/emission (Ex/Em) wavelengths of 230/335, 285/335, 365/475, and 435/495 nm. Furthermore, second derivative synchronous fluorescence (SDSF) spectra were extracted from the EEMs, and partial least square regression and support vector machine models were developed on each of the EEMs and SDSF spectra. The R2 values reached 0.86 and 0.74 for protein and oil, respectively. From the loading spectra, fluorescence at Ex/Em of 230-285/335 nm and 350/500 nm mainly contribute to the protein and oil content prediction, respectively. Those results revealed the potential of fluorescence spectroscopy as a tool for a rapid prediction of soybean protein and oil content.

Antioxidant Effect of Lactobacillus fermentum CQPC04-Fermented Soy Milk on D-Galactose-Induced Oxidative Aging Mice

The aim of this study is to evaluate the changes in soy isoflavones and peptides in soy milk after lactic acid bacterial fermentation, and explore the positive effects of fermented soy milk on an oxidative aging mouse model induced with D-galactose. We found that free soybean isoflavones and peptides increased after soy milk was fermented by Lactobacillus fermentum CQPC04. The in vivo results indicated that L. fermentum CQPC04-fermented soy milk enhanced the organ index of the liver and spleen, and improved the pathological morphology of the liver, spleen, and skin. L. fermentum CQPC04-fermented soy milk increased the enzymatic activity of glutathione peroxidase (GSH-Px), total superoxide dismutase (T-SOD), and catalase (CAT), increased glutathione (GSH), but decreased the levels of nitric oxide (NO) and malondialdehyde (MDA) in serum, liver, and brain tissues of oxidative aging mice. The above mentioned fermented soy milk also increased the levels of collagen I (Col I), hyaluronic acid (HA), and collagen III (Col III), and decreased the levels of advanced glycation End products (AGEs) and hydrogen peroxide (H₂O₂). The RT-qPCR results showed that L. fermentum CQPC04-fermented soy milk upregulated the mRNA expression of nuclear factor erythroid 2?related factor (Nrf2), heme oxygenase-1 (HMOX1), quinone oxido-reductase 1 (Nqo1), neuronal nitric oxide synthase (NOS1), endothelial nitric oxide synthase (NOS3), Cu/Zn–superoxide dismutase (Cu/Zn-SOD), Mn–superoxide dismutase (Mn-SOD), and CAT, but downregulated the expression of inducible nitric oxide synthase (NOS2) and glutamate cysteine ligase modifier subunit (Gclm) in liver and spleen tissues. Lastly, the fermented soy milk also increased the gene expression of Cu/Zn-SOD, Mn-SOD, CAT, GSH-Px, matrix metalloproteinases 1 (TIMP1), and matrix metalloproteinases 2 (TIMP2), and decreased the expression of matrix metalloproteinase 2 (MMP2) and matrix metalloproteinase 9 (MMP9) in skin tissue. In conclusion, L. fermentum CQPC04-fermented soy milk was able to satisfactorily delay oxidative aging effects, and its mechanism may be related to the increase in free soy isoflavones and peptides.

In sacco evaluation of ruminal degradability of isoflavones from full-fat soybean and extracted soybean meal-A pilot study

The aim of the study was to determine the ruminal degradability of dry matter (DM), daidzein, genistein, glycitein and total isoflavones in ground full-fat soybean (GFFS) and solvent-extracted soybean meal (SSBM) using the in sacco method. The experiment was carried out in three replications on ruminally cannulated sheep that were fed twice a day with a diet consisted of hay and supplemental mixture (6:4, DM basis). The nylon bags with 2 g feed samples ground to 2 mm were incubated in the rumen for 0, 2, 4, 8, 16 and 24 h. The effective degradability (ED) of DM, daidzein, genistein, glycitein and total isoflavones was calculated at outflow rate of 0.06 h. The ED of DM in GFFS was 77.8% and was higher than in SSBM being 71.8% (p < 0.001). The ED of daidzein (96.8%) and genistein (93.6%) was higher for SSBM compared with GFFS (93.9% and 92.8%, p < 0.001 and p = 0.003, respectively) while ED of glycitein was lower for SSBM than for GFFS (75.5 and 81.7%, respectively, p < 0.001). All isoflavones in the incubations were extensively degraded in the rumen, and regardless of dietary source, they were almost completely degraded after 16 h of incubation. Further, the disappearance patterns, that is the functions describing the time courses of the analyte disappearance, were assessed. The disappearance patterns of daidzein, genistein, glycitein and total isoflavones were similar and showed greater disappearance of mentioned isoflavones from SSBM compared to GFFS (p < 0.001 for daidzein, genistein and total isoflavones and p = 0.002 for glycitein). The study provides knowledge on the effect of processing on degradability of isoflavones in rumen that can be used to clarify the interrelationship between isoflavones and rumen microbiota.

Rapid qualitative identification and quantitative analysis of Flos Mume based on Fourier transform near infrared spectroscopy

Flos Mume, an ancient Chinese plant, is widely used for food and medicine. There are numerous varieties of Flos Mume, whose main active components are chlorogenic acid, hyperoside and isoquercitrin. Currently, Flos Mume varieties are mainly distinguished by physical appearance and they have not been scientifically indexed for identification. Fourier transform near infrared spectroscopy (FT-NIR) is a technique that when combined with chemometrics, determines internal components of samples and classifies them. Here, to distinguish between different Flos Mume varieties, we used a qualitative identification model based on FT-NIR. Various model parameters indicated its stability and high predictive performance. We developed a rapid, non-destructive method of simultaneously analyzing 8 components but found that only neochlorogenic acid, chlorogenic acid, rutin, hyperoside, and isoquercitrin have application value. Other components were excluded due to low concentration and poor prediction. Chemometric analysis found that chlorogenic acid become an ingredient which is quite different in the different categories. The content of chlorogenic acid were the highest among these components. Different varieties of Flos Mume were distinguished based on chlorogenic acid content, indicating that chlorogenic acid has potential to become a key indicator for application in quality evaluation. The established FT-NIR model for chlorogenic acid detection had excellent predictive capacity. FT-NIR was the first time applied to Flos Mume and our findings offer theoretical reference for the rapid identification and quantitative analysis of Flos Mume based on FT-NIR. Flos Mume could be evaluated for quality quickly and easily by means of FT-NIR spectroscopy.

Production of Bovine Equol-Enriched Milk: A Review

Simple Summary

Milk and dairy products contain many substances beneficial to human health; moreover, the contents of some of these substances can be enhanced. This is also the case of isoflavones which are compounds of plant origin that can be ingested and metabolized by cattle and, subsequently, secreted into bovine milk. An especially healthful substance called equol is ranked among isoflavone metabolites, commonly produced in the digestive tract of cattle. Equol content in milk can be modified by using feedstuffs with different contents of isoflavones or by milk processing and storage.

Abstract

Milk and dairy products are important sources of nutrients in the human diet because they contain a number of essential substances and other biologically active components. Many of these substances can be modified, and thus offer opportunities to use milk and dairy products as functional food. Isoflavones are particularly important in human nutrition due to their diverse pharmacological and antioxidant properties. The clinical effectiveness of isoflavone-rich products is believed to be dependent on their ability to metabolize daidzein to equol, which may directly exert cancer preventive effects. However, only approximately 30–40% of humans are able to produce equol, while animals, in general, produce equol. Equol is the predominant product of bacterial metabolism of isoflavones and can be found in various amounts in some food of animal origin, especially in milk. Therefore, milk and dairy products can be considered to be sources of equol for humans who are not able to produce this metabolite. When the content of isoflavones in milk is to be modified, two groups of factors should be considered, i.e., dietary factors that include the source of isoflavones and the processing effects on feedstuffs and animal factors that include the intake of isoflavones, ruminal and postruminal changes, and the health and physiological status of animals. The approximate content of isoflavones in milk can be predicted using carry-over rates for different dietary sources or using a formula that describes the relationship between equol concentration in milk and formononetin intake. Processing and storage can affect the content and profile of isoflavones in milk and dairy products.

Infrared spectroscopy technique for quantification of compounds in plant-based medicine and supplement

Quality control of plant-based medicine and supplements must be carried out to ensure uniformity in quality and safety in their use, resulting in the need for effective and accurate analytical methods. Infrared spectroscopy is a method of qualitative and quantitative analysis that is fast, time-saving, cost-effective,accurate, and nondestructive. This method has been applied for quantitative analysis of compounds in complex matrices such as plant-based medicine and supplements supported by chemometrics techniques. The success of infrared spectroscopy applications for quantitative analysis of phytochemicals and adulterants content in plant-based medicine and supplement can happen by several factors. This article highlights the effect of spectral preprocessing and variable selection on quantitative analysis of phytochemical and adulterant in plant-based medicine and supplements using infrared spectroscopy. Literature search was conducted with PubMed, Google Scholar, and Science Direct by selecting quantitative analysis research on plant-based medicines and supplements that utilize spectral preprocessing techniques and variable selection in processing data analysis. The preprocessing spectra and variables selection can affect the accuracy and precision of infrared spectroscopy methods. The variable selection can be done using the wavenumber point technique, the wavenumber interval, or a combination thereof. Variable selection is more commonly used for near-infrared data than for IR data. The optimization of the preprocessing spectra and variables selection technique will be useful in increasing the ability of infrared spectroscopy in predicting compound levels.

Feasibility of rapid piperine quantification in whole and black pepper using near infrared spectroscopy and chemometrics

Piperine is a bioactive alkaloid that possesses various health benefits and is responsible for the pungent aroma of pepper. Piperine content in whole and ground black pepper (n = 132) was analyzed by near-infrared spectroscopy (NIRS) in the 950 to 1650 nm wavelength window. Chemometric modeling using partial least square regression was performed, and outliers were checked and removed during the preparation of the calibration curve by considering sample residual variance and sample leverage. Model accuracy was evaluated with a low root-mean-square error of cross-validation (RMSECV) and a high ratio performance to deviation (RPD). The optimal model had a coefficient of determination (R² ) of 0.726, RMSECV of 0.289 g/100 g, and RPD of 1.744 for the whole black pepper. The results of R² , RMSECV, and RPD for the ground black pepper were 0.850, 0.231 g/100 g, and 2.424, respectively. Therefore, based on the perspective of onsite process, the proposed NIRS method can be employed for selecting abnormal samples during the inspection of black pepper raw material and for quantifying piperine contents of final black pepper product. PRACTICAL APPLICATION: Generally, the quality indicators of black pepper are graded solely based on their external appearance, quality, and size. This study discloses the development of a near-infrared spectroscopy-based fast and accurate nondestructive analytical method for the detection of piperine, a bioactive constituent of pepper, as a tool for the quality control of whole and ground black pepper.