Rapid determination of total phenolic content of whole wheat flour using near-infrared spectroscopy and chemometrics

A strategy to improve the detection accuracy and universality of highland barley muti-quality attributes based on near infrared spectroscopy combined with model transfer method

This study utilized direct standardization (DS), piecewise direct standardization (PDS), and DS-PDS algorithms to facilitate model transfer across instruments during the detection of highland barley (HB), and to calibrate HB samples in various states. Following the model transfer, the slave instrument results showed significant improvement. After DS processing, optimal results were achieved for total starch (prediction set correlation coefficient, Rp = 0.903), amylose (Rp = 0.936), β-glucan (Rp = 0.929). After DS-PDS processing, the best results were observed for protein (Rp = 0.855) and total phenols (Rp = 0.937). Moreover, after scatter correction, the results for grains were further enhanced. Following DS treatment, total starch (Rp = 0.873), amylose (Rp = 0.950), protein (Rp = 0.899), β-glucan (Rp = 0.899), and total phenols (Rp = 0.965) demonstrated optimal performance. Overall, this study significantly enhanced the universality and accuracy of models through effective model transfer and scatter correction for grains.

Chemometrics-powered spectroscopic techniques for the measurement of food-derived phenolics and vitamins in foods: A review

Foods are rich in various bioactive compounds, like phenolics, and vitamins, which play important physiological roles in the human body. The analysis of phenolics and vitamins in plant and animal-based foods is a topic of growing interest. Compared with conventional methods, the chemometrics-powered infrared, Fourier transform-near infrared and mid-infrared, ultraviolet-visible, fluorescence, and Raman spectroscopy offer a reliable, low-cost, and nondestructive means to determine phenolics and vitamins. This study briefly presents the physical properties of phenolics and vitamins and their physiological benefits, features of commonly used spectroscopic techniques, sample preparation for spectroscopic data analysis, and the progress of chemometrics methods for model calibration using spectroscopic data and their primary challenges in predicting phenolics and vitamins in real samples for the last five years. The spectral preprocessing method combined feature extraction quantitative chemometric model comparatively showed the best results for simultaneous and single detection. Finally, this study put forward future directions.

Lightweight deep learning algorithm for real-time wheat flour quality detection via NIR spectroscopy

Wheat flour quality, determined by factors such as protein and moisture content, is crucial in food production. Traditional methods for analyzing these parameters, though precise, are time-consuming and impractical for large-scale operations. This study presents a lightweight convolutional neural network designed for real-time wheat flour quality monitoring using near-infrared spectroscopy. The model incorporates Ghost bottlenecks, external attention modules, and the Kolmogorov-Arnold network to enhance feature extraction and improve prediction accuracy. Testing results demonstrate high predictive performance with R2 values of 0.9653 (RMSE: 0.2886 g/100 g, RPD: 5.8981) for protein and 0.9683 (RMSE: 0.3061 g/100 g, RPD: 5.1046) for moisture content. The model's robustness across diverse samples and its suitability for online applications make it a promising tool for efficient and non-destructive quality control in the food industry.

Nutritional enhancement in black seed (Nigella sativa L.) using bacteria-based biofertilizers

Nigella sativa L. is an aromatic spice, utilized as an original and peculiar flavoring ingredient in a variety of culinary applications and pharmaceuticals. Black seed (Nigella sativa L.) belongs to the family Ranunculaceae. It is an undercultivated crop in Pakistan. The present study was planned keeping in mind sustainable development goals SDG 3 (good health and well-being) and SDG 15 (life on land). The effects of several rhizospheric bacterial strains and synthetic fertilizers on the development of N. sativa and nutrition were studied using a completely randomized experimental design. For this purpose, plant growth-promoting effects of different strains (Azospirillum brasilense, Azospirillum lipoferum, and Pantoea agglomerans) and synthetic fertilizers (nitrogen and phosphorus) were assembled to check their effects individually and in combination form. Azospirillum lipoferum and Pantoea agglomerans inoculation significantly enhanced the morphological characteristics of N. sativa, whether applied individually or in combination, with positive effects on seedlings, plant height, number of branches, number of leaves, number of flowers, stamens numbers, follicles number, number of tentacles and seed production. N. sativa plants that were simultaneously inoculated with Azospirillum lipoferum and Pantoea agglomerans showed the highest potential for antioxidant activity, particularly in petroleum ether extracts. In the methanolic extract, a higher amount of radical scavenging was observed as compared to positive and negative control. There was also increase in fat, moisture and carbohydrate contents of the combination inoculated plant. So, from the present study, in Pakistan, the technique is recommended to enhance the yield and nutritional value of N. sativa.

Quantitative predictions of protein and total flavonoids content in Tartary and common buckwheat using near-infrared spectroscopy and chemometrics

A rapid method was developed for determining the total flavonoid and protein content in Tartary buckwheat by employing near-infrared spectroscopy (NIRS) and various machine learning algorithms, including partial least squares regression (PLSR), support vector regression (SVR), and backpropagation neural network (BPNN). The RAW-SPA-CV-SVR model exhibited superior predictive accuracy for both Tartary and common buckwheat, with a high coefficient of determination (R2p = 0.9811) and a root mean squared error of prediction (RMSEP = 0.1071) for flavonoids, outperforming both PLSR and BPNN models. Additionally, the MMN-SPA-PSO-SVR model demonstrated exceptional performance in predicting protein content (R2p = 0.9247, RMSEP = 0.3906), enhancing the effectiveness of the MMN preprocessing technique for preserving the original data distribution. These findings indicate that the proposed methodology could efficiently assess buckwheat adulteration analysis. It can also provide new insights for the development of a promising method for quantifying food adulteration and controlling food quality.

In-Vitro Antimicrobial Activities of Grape Seed, Green Tea, and Rosemary Phenolic Extracts Against Liver Abscess Causing Bacterial Pathogens in Cattle

Liver abscesses, which occur in finishing cattle, are of significant economic concern to the feedlot industry. The causative agents include both Fusobacterium necrophorum subspecies (F. necrophorum and F. funduliforme), Trueperella pyogenes (T. pyogenes), and Salmonella enterica serotype Lubbock (S. Lubbock). Tylosin, a macrolide antibiotic, is supplemented in the feed to reduce liver abscesses. However, due to the concern with emergence of antimicrobial resistance, the antimicrobial activities of the plant-based phenolic compounds could be an antibiotic alternative to control liver abscesses. We investigated the inhibitory activities of phenolic compounds extracted from grape seed, green tea, and rosemary on liver-abscess-causing bacterial pathogens. Total phenolic content was determined spectrophotometrically. Anaerobic Brain-Heart Infusion broth (for Fusobacterium) and Muller-Hinton broth (for S. enterica and T. pyogenes) with phenolic extracts at 0, 0.1, 1, and 2 mg/mL were prepared. Growth was measured at 0, 12, 24 and 48 h by determining bacterial concentrations. A micro-broth dilution method was used to quantify the inhibition. Grape seed and green tea phenolics inhibited growth of both Fusobacterium subspecies, T. pyogenes and S. enterica. Green tea at 1 mg/mL concentration was more effective in inhibiting the growth of Fusobacterium when compared to grape seed and rosemary. Green tea at 2 mg/mL was more effective than at 1 mg/mL against Salmonella. The inhibitory effect was dose-dependent, which was consistent across all strains within the same bacterial species. The phenolic extracts were inhibitory against T. pyogenes with minimum inhibitory concentration ranging from 6.25 to 12.5 µg/mL. Among the phenolic extracts tested, green tea showed the most potent activity, suggesting its strong potential as a natural alternative to conventional antibiotics. Plant-based phenolic compounds supplemented in the feed may have the potential to control liver abscesses.

Application of ATR-FTIR and FT-NIR spectroscopy coupled with chemometrics for species identification and quality prediction of boletes

The taste and aroma of edible mushrooms, which is a criterion of judgment for consumer purchases, are influenced by amino acids and their metabolites. Sixty-eight amino acids and their metabolites were identified using liquid chromatography mass spectrometry (LC-MS), and 16 critical marker components were screened. The chemical composition of different species of boletes was characterized by two-dimensional correlation spectroscopy (2DCOS) to determine the sequence of molecular vibrations or group changes. Identification of boletes species based on partial least squares discrimination (PLS-DA) combined with Fourier transform near-infrared spectroscopy (FT-NIR) and Fourier transform infrared spectroscopy (ATR-FTIR), residual convolutional neural network (ResNet) combined with three-dimensional correlation spectroscopy (3DCOS) was performed with 100% accuracy. Partial least squares regression (PLSR) analysis showed that FT-NIR and ATR-FTIR spectra were highly correlated with the amino acids and their metabolites detected by LC-MS. All models had achieved an R2p of 0.911 and an RPD >3.0. The results show that FT-NIR and ATR-FTIR spectroscopy in combination with chemometrics methods can be used for rapid species identification and estimation of amino acids and their metabolites content in boletes. This study provides new techniques and ideas for the authenticity of species information and the quality assessment of boletes.

Investigation on the exopolysaccharide production from blueberry juice fermented with lactic acid bacteria: Optimization, fermentation characteristics and Vis-NIR spectral model

The exopolysaccharide production from blueberry juice fermented were investigated. The highest exopolysaccharide yield of 2.2 ± 0.1 g/L (increase by 32.5 %) was reached under the conditions of temperature 26.5 °C, pH 5.5, inoculated quantity 5.4 %, and glucose addition 9.1 % using the artificial neural network and genetic algorithm. Under the optimal conditions, the viable cell counts and total acids were increased by 2.0 log CFU/mL and 1.6 times, respectively, while the content of phenolics and anthocyanin was decreased by 9.26 % and 7.86 %, respectively. The changes of these components affected the exopolysaccharide biosynthesis. The absorption bands of -OH and -CH associated with the main functional groups of exopolysaccharide were detected by Visible near-infrared spectroscopy. The prediction model based on spectrum results was constructed. Competitive adaptive reweighted sampling and the random forest were used to enhance the model's prediction performance with the value of RC = 0.936 and RP = 0.835, indicating a good predictability of exopolysaccharides content during fermentation.

Effect of drying temperature on composition of edible mushrooms: Characterization and assessment via HS-GC-MS and IR spectral based volatile profiling and chemometrics

Edible wild mushrooms are one of the popular ingredients due to their high quality and unique flavor and nutrients. To gain insight into the effect of drying temperature on its composition, 86 Boletus bainiugan were divided into 5 groups and dried at different temperatures. Headspace solid-phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS) was used for the identification of volatile organic compounds (VOCs) of Boletus bainiugan. The 21 differential VOCs that distinguish different drying temperatures of Boletus bainiugan were identified. 65 °C retained more VOCs. There were differences in their types and content at different temperatures, proteins, polysaccharides, crude fibers, and fats. Fourier transform near-infrared (FT-NIR) spectroscopy, Fourier transform infrared (FTIR) spectroscopy, and two-dimensional correlation spectroscopy (2DCOS) images were successfully characterized for differences in the chemical composition of Boletus bainiugan. Partial least squares discriminant analysis (PLS-DA) verified the variability in the chemical composition of Boletus bainiugan with the coefficient of determination (R2) = 0.95 and predictive performance (Q2) = 0.75 with 92.31% accuracy. Next, infrared spectroscopy provides a fast and efficient assessment of the content of Boletus bainiugan nutrients (proteins, polysaccharides, crude fibers, and fats).

Near-Infrared Spectroscopic Determination of Pentacyclic Triterpenoid Concentrations in Additives for Animal Food

The nutritional composition of food for animal production can be enhanced using olive tree and plant by-products due to their high content of bioactive compounds such as pentacyclic triterpenes. Here, we present a novel application of near-infrared spectroscopy (NIRS) for the prediction of the total or individual [maslinic acid (MA), oleanolic acid (OA), and uvaol (UO)] pentacyclic triterpene concentrations in a feed additive obtained from a plant mixture. The oxygen radical absorbance capacity of these types of samples demonstrated the existence of a high antioxidant capacity. The conventional determination methods of pentacyclic triterpene concentration are costly, labor-intensive, and not practical for analyzing several lines within a limited timeframe at the factory level. The optimal regression model developed in our work demonstrated high correlation values for the calibration and validation sets, along with a high residual prediction deviation value. We used 63 samples for the development of the model. The NIRS method can be applied directly to dried powder and makes extraction and high-performance liquid chromatography (HPLC) analysis unnecessary. Our results also demonstrate that NIRS can accurately quantify pentacyclic triterpenes even at low concentrations in food additives. It can be used at the factory level to directly determine the pentacyclic triterpene concentrations in the additive powder at the same time that the powder is produced.

Challenges to Increasing Dietary Fiber in White Flour and Bread

Increasing the intake of dietary fiber from staple foods is a key strategy to improve the health of consumers. White bread is an attractive vehicle to deliver increased fiber as it is widely consumed and available to all socio-economic groups. However, fiber only accounts for about 4% of the dry weight of white flour and bread compared to 10-15% in whole grain bread and flour. We therefore discuss the challenges and barriers to developing and exploiting new types of wheat with high fiber content in white flour. These include defining and quantifying individual fiber components and understanding how they are affected by genetic and environmental factors. Rapid high throughput assays suitable for determining fiber content during plant breeding and in grain-utilizing industries are urgently required, while the impact of fiber amount and composition on flour processing quality needs to be understood. Overcoming these challenges should have significant effects on human health.

Synthesis of Guanine/Vermiculite Two-Dimensional Nanocomposites for Wireless Humidity Sensing in Nut Storage Environment

Two-dimensional (2D) nanostructures have the advantages of high specific surface area, easy surface functionalization, abundant active sites, and good compatibility with device integration and can be assembled into three-dimensional structures, which are key to the development of high-performance gas sensors. In this study, 2D vermiculite (VMT) nanosheets and guanine (G), two renewable resources with unique chemical structures, were organically combined to fully use the specificity of their molecular structures and functional activities. Driven by the regulation of 2D VMT nanosheets, guanine/vermiculite (G/VMT)-based 2D nanocomposites with controllable pore structure, multiple binding sites, and unobstructed mass transfer were designed and synthesized. The G/VMT nanocomposite material was used as a quartz crystal microbalance (QCM) electrode-sensitive film material to build a QCM-based humidity sensor. G/VMT-based QCM humidity sensor had good logarithmic linear relation (0.9971), high sensitivity (24.49 Hz/% relative humidity), low hysteresis (1.75% RH), fast response/recovery time (39/6 s), and good stability. Furthermore, with a QCM sensor and a specially designed wireless circuit, a wireless humidity detection system transmitting via Wi-Fi allows real-time monitoring of nut storage. This study presents an environmentally friendly, high-performance, miniature 2D nanocomposite sensor strategy for real-time monitoring.

Discrimination of wheat flour grade based on PSO-SVM of hyperspectral technique

Rapid detection of wheat flour grade played an important role in the food industry. In this work, hyperspectral technology was used to detect five types of wheat flour. An analysis model was established based on the reflectance of samples at 968 ∼ 2576 nm. Moreover, multivariate scattering correction (MSC), standard normalized variate (SNV), and Savitzky-Golay (S-G) convolution smoothing were used for preprocessing, which was employed to reduce the influence of noise in the original spectrum. In order to simplify the model, competing adaptive reweighted sampling (CARS), successive projection algorithm (SPA), uninformative variable elimination (UVE) and the UVE-CARS algorithm were applied to extract feature wavelengths. Both partial least squares discriminant analysis (PLS-DA) model and support vector machine (SVM) model were established according to feature wavelengths. Furthermore, particle swarm optimization (PSO) algorithm was adopted to optimize the search of SVM model parameters, such as the penalty coefficient c and the regularization coefficient g. Experimental results suggested that the non-linear discriminant model for wheat flour grades was better than the linear discriminant model. It was considered that the MSC-UVE-CARS-PSO-SVM model achieved the best forecasting results for wheat flour grade discrimination, with 100% accuracy both in the calibration set and the validation set. It further shows that the classification of wheat flour grade can be effectively realized by using the hyperspectral and SVM discriminant analysis model, which proves the potential of hyperspectral reflectance technology in the qualitative analysis of wheat flour grade.

Acetone extracts of Berberis vulgaris and Cornus mas L. induce apoptosis in MCF-7 breast cancer cells

Background/aim

This study aimed to determine the proliferation and apoptotic effects of extracts from Cornus mas L. and Berberis vulgaris fruits on human breast cancer cells (MCF-7).

Materials and methods

The Cornus mas L. and Berberis vulgaris fruits, which constitute the herbal material of the study, were turned into 80% acetone extract after washing. The total phenolic content in Berberis vulgaris fruit extracts was determined calorimetrically using Folin-Ciocalteu reagent. The spectrophotometric method was used to determine the total flavonoid amount of the extracts. In order to measure the antioxidant capacity of Cornus mas L. and Berberis vulgaris fruits and extracts, DPPH Radical Scavenging Power test and Cu (II) ion reducing antioxidant capacity method were applied. Cell viability rates were determined by the XTT method. Flow cytometric measurement was performed to examine the apoptotic role of the extracts in the cell by using the Annexin-V/7-AAD commercial kit.

Results

According to the data, Berberis vulgaris fruit extract appeared more effective on MCF-7 breast cancer cells in both 24 and 48 hours of exposure. Analyses made to examine the phenolic component and antioxidant capacity properties of the fruits used in the study and the results we encountered when we exposed the cell were found to be compatible with each other. Annexin-V/7-AAD method showed that the apoptotic effects of the extracts in 48 hour exposures were more effective.

Conclusion

It has been determined that Cornus mas L. and Berberis vulgaris fruits, which are rich in phenolic components with high flavonoid content and high antioxidant capacities, support the apoptosis of cancer cells.

Cold Plasma-Assisted Extraction of Phytochemicals: A Review

In recent years, there has been growing interest in bioactive plant compounds for their beneficial effects on health and for their potential in reducing the risk of developing certain diseases such as cancer, cardiovascular diseases, and neurodegenerative disorders. The extraction techniques conventionally used to obtain these phytocompounds, however, due to the use of toxic solvents and high temperatures, tend to be supplanted by innovative and unconventional techniques, in line with the demand for environmental and economic sustainability of new chemical processes. Among non-thermal technologies, cold plasma (CP), which has been successfully used for some years in the food industry as a treatment to improve food shelf life, seems to be one of the most promising solutions in green extraction processes. CP is characterized by its low environmental impact, low cost, and better extraction yield of phytochemicals, saving time, energy, and solvents compared with other classical extraction processes. In light of these considerations, this review aims to provide an overview of the potential and critical issues related to the use of CP in the extraction of phytochemicals, particularly polyphenols and essential oils. To review the current knowledge status and future insights of CP in this sector, a bibliometric study, providing quantitative information on the research activity based on the available published scientific literature, was carried out by the VOSviewer software (v. 1.6.18). Scientometric analysis has seen an increase in scientific studies over the past two years, underlining the growing interest of the scientific community in this natural substance extraction technique. The literature studies analyzed have shown that, in general, the use of CP was able to increase the yield of essential oil and polyphenols. Furthermore, the composition of the phytoextract obtained with CP would appear to be influenced by process parameters such as intensity (power and voltage), treatment time, and the working gas used. In general, the studies analyzed showed that the best yields in terms of total polyphenols and the antioxidant and antimicrobial properties of the phytoextracts were obtained using mild process conditions and nitrogen as the working gas. The use of CP as a non-conventional extraction technique is very recent, and further studies are needed to better understand the optimal process conditions to be adopted, and above all, in-depth studies are needed to better understand the mechanisms of plasma-plant matrix interaction to verify the possibility of any side reactions that could generate, in a highly oxidative environment, potentially hazardous substances, which would limit the exploitation of this technique at the industrial level.

Rapid determination of triglyceride and glucose levels in Drosophila melanogaster induced by high-sugar or high-fat diets based on near-infrared spectroscopy

Triglyceride and glucose levels are important indicators for determining metabolic syndrome, one of the leading public-health burdens worldwide. Drosophila melanogaster is an ideal model for investigating metabolic diseases because it has 70% homology to human genes and its regulatory mechanism of energy metabolism homeostasis is highly similar to that of mammals. However, traditional analytical methods of triglyceride and glucose are time-consuming, laborious, and costly. In this study, a simple, practical, and reliable near-infrared (NIR) spectroscopic analysis method was developed for the rapid determination of glucose and triglyceride levels in an in vivo model of metabolic disorders using Drosophila induced by high-sugar or high-fat diets. The partial least squares (PLS) model was constructed and optimized using different spectral regions and spectral pretreatment methods. The overall results had satisfactory prediction performance. For Drosophila induced by high-sugar diets, the correlation coefficient (R_P) and root mean square error of prediction (RMSEP) were 0.919 and 0.228 mmoL gprot^-1 for triglyceride and 0.913 and 0.143 mmoL gprot^-1 for glucose respectively; for Drosophila induced by high-fat diets, the R_P and RMSEP were 0.871 and 0.097 mmoL gprot^-1 for triglyceride and 0.853 and 0.154 mmoL gprot^-1 for glucose, respectively. This study demonstrated the potential of using NIR spectroscopy combined with PLS in the determination of triglyceride and glucose levels in Drosophila, providing a rapid and effective method for monitoring metabolite levels during disease development and a possibility for evaluating metabolic diseases in humans in clinical practice.

An integrated strategy of spectrum-effect relationship and near-infrared spectroscopy rapid evaluation based on back propagation neural network for quality control of Paeoniae Radix Alba

The quantitative analysis of near-infrared spectroscopy in traditional Chinese medicine has still deficiencies in the selection of the measured indexes. Then Paeoniae Radix Alba is one of the famous "Eight Flavors of Zhejiang" herbs, however, it lacks the pharmacodynamic support, and cannot reflect the quality of Paeoniae Radix Alba accurately and reasonably. In this study, the spectrum-effect relationship of the anti-inflammatory activity of Paeoniae Radix Alba was established. Then based on the obtained bioactive component groups, the genetic algorithm, back propagation neural network, was combined with near-infrared spectroscopy to establish calibration models for the content of the bioactive components of Paeoniae Radix Alba. Finally, three bioactive components, paeoniflorin, 1,2,3,4,6-O-pentagalloylglucose, and benzoyl paeoniflorin, were successfully obtained. Their near-infrared spectroscopy content models were also established separately, and the validation sets results showed the coefficient of determination (R² > 0.85), indicating that good calibration statistics were obtained for the prediction of key pharmacodynamic components. As a result, an integrated analytical method of spectrum-effect relationship combined with near-infrared spectroscopy and deep learning algorithm was first proposed to assess and control the quality of traditional Chinese medicine, which is the future development trend for the rapid inspection of traditional Chinese medicine.

Effect of Layered Debranning Processing on the Proximate Composition, Polyphenol Content, and Antioxidant Activity of Whole Grain Wheat

Layered debranning processing (LDP) of whole grain wheat (WGW) could not only retain more bioactive compounds but also contributes to grain saving policy as compared with the refined wheat flour (WF). In this study, effect of different debranning rates from 0 to 13.37% on the proximate composition, polyphenol content, and the antioxidant activity were analyzed. As debranning rates increased from 0 to 13.37%, the insoluble dietary fiber content decreased from 9.94% to 6.47%, whereas the soluble dietary fiber contents increased from 3.06% to 3.98%. The free phenolic content decreased by 62.72%, while the free flavonoid content increased by 4.68% with debranning rates increasing. For the phenolic acids, protocatechuic acid and ferulic acid dominated the free and bound phenolic acid in WGW, which showed the highest contents at 6.95% and 4.45% debranning rates, respectively. As for flavonoids, naringenin (the free-state phenolic) and rutin (the bound state phenolic) in WGW had the greatest level at 4.45% debranning rate. As compared to WGW and WF, LDP significantly improved the DPPH, ABTS·+ radical scavenging activities and total antioxidant activities. In conclusion, 4.45% and 6.95% were the best debranning rates to retain polyphenol contents and antioxidant activities.

Non-destructive characterization of pulse flours-A review

The consumption of plant-based proteins sourced from pulses is sustainable from the perspective of agriculture, environment, food security, and nutrition. Increased incorporation of high-quality pulse ingredients into foods such as pasta and baked goods is poised to produce refined food products to satisfy consumer demand. However, a better understanding of pulse milling processes is required to optimize the blending of pulse flours with wheat flour and other traditional ingredients. A thorough review of the state-of-the-art on pulse flour quality characterization reveals that research is required to elucidate the relationships between the micro- and nanoscale structures of these flours and their milling-dependent properties, such as hydration, starch and protein quality, components separation, and particle size distribution. With advances in synchrotron-enabled material characterization techniques, there exist a few options that have the potential to fill knowledge gaps. To this end, we conducted a comprehensive review of four high-resolution nondestructive techniques (i.e., scanning electron microscopy, synchrotron X-ray microtomography, synchrotron small-angle X-ray scattering, and Fourier-transformed infrared spectromicroscopy) and a comparison of their suitability for characterizing pulse flours. Our detailed synthesis of the literature concludes that a multimodal approach to fully characterize pulse flours will be vital to predicting their end-use suitability. A holistic characterization will help optimize and standardize the milling methods, pretreatments, and post-processing of pulse flours. Millers/processors will benefit by having a range of well-understood pulse flour fractions to incorporate into food formulations.

Prediction of Mineral Composition in Wheat Flours Fortified with Lentil Flour Using NIR Technology

Lentil flour is an important source of minerals, including iron, so its use in food fortification programs is becoming increasingly important. In this study, the potential of near infrared technology to discriminate the presence of lentil flour in fortified wheat flours and the quantification of their mineral composition is evaluated. Three varieties of lentils (Castellana, Pardina and Guareña) were used to produce flours, and a total of 153 samples of wheat flours fortified with them have been analyzed. The results show that it is possible to discriminate fortified flours with 100% efficiency according to their lentil flour content and to discriminate them according to the variety of lentil flour used. Regarding their mineral composition, the models developed have shown that it is possible to predict the Ca, Mg, Fe, K and P content in fortified flours using near infrared spectroscopy. Moreover, these models can be applied to unknown samples with results comparable to ICP-MS determination of these minerals.

Structural Analysis and Classification of Low-Molecular-Weight Hyaluronic Acid by Near-Infrared Spectroscopy: A Comparison between Traditional Machine Learning and Deep Learning

Confusing low-molecular-weight hyaluronic acid (LMWHA) from acid degradation and enzymatic hydrolysis (named LMWHA-A and LMWHA-E, respectively) will lead to health hazards and commercial risks. The purpose of this work is to analyze the structural differences between LMWHA-A and LMWHA-E, and then achieve a fast and accurate classification based on near-infrared (NIR) spectroscopy and machine learning. First, we combined nuclear magnetic resonance (NMR), Fourier transform infrared (FTIR) spectroscopy, two-dimensional correlated NIR spectroscopy (2DCOS), and aquaphotomics to analyze the structural differences between LMWHA-A and LMWHA-E. Second, we compared the dimensionality reduction methods including principal component analysis (PCA), kernel PCA (KPCA), and t-distributed stochastic neighbor embedding (t-SNE). Finally, the differences in classification effect of traditional machine learning methods including partial least squares-discriminant analysis (PLS-DA), support vector classification (SVC), and random forest (RF) as well as deep learning methods including one-dimensional convolutional neural network (1D-CNN) and long short-term memory (LSTM) were compared. The results showed that genetic algorithm (GA)-SVC and RF were the best performers in traditional machine learning, but their highest accuracy in the test dataset was 90%, while the accuracy of 1D-CNN and LSTM models in the training dataset and test dataset classification was 100%. The results of this study show that compared with traditional machine learning, the deep learning models were better for the classification of LMWHA-A and LMWHA-E. Our research provides a new methodological reference for the rapid and accurate classification of biological macromolecules.

Accumulation of Wheat Phenolic Acids under Different Nitrogen Rates and Growing Environments

The health benefits of whole wheat grains are partially attributed to their phenolic acid composition, especially that of trans-ferulic acid (TFA), which is a powerful natural antioxidant. Breeders and producers are becoming interested in wheat with enhanced health-promoting effects. This study investigated the effects of different nitrogen (N) application rates (0, 42, 84, 126, and 168 N kg ha^-1) on the phenolic acid composition of three wheat varieties in four locations for two years. The results indicate that the different N rates did not affect the TFA concentration but that they significantly affected the concentrations of para-coumaric acid, sinapic acid, and cis-ferulic acid in the wheat grains. A statistical analysis suggested that the wheat phenolic acid composition was predominantly determined by wheat variety, though there existed some interaction effect between the wheat variety and environments. The TFA concentration of the variety Jimai 22 was generally higher (with a mean value of 726.04 µg/g) but was easily affected by the environment, while the TFA concentration of the variety Zhongmai 578 (with a mean value of 618.01 µg/g) was more stable across the different environments. The results also suggest that it is possible to develop new wheat varieties with high yield potential, good end-use properties, and enhanced nutraceutical values.

Quantitative assessment of wheat quality using near-infrared spectroscopy: A comprehensive review

Wheat is one of the most widely cultivated crops throughout the world. A great need exists for wheat quality assessment for breeding, processing, and products production purposes. Near-infrared spectroscopy (NIRS) is a rapid, low-cost, simple, and nondestructive assessment method. Many advanced studies associated with NIRS for wheat quality assessment have been published recently, either introducing new chemometrics or attempting new assessment parameters to improve model robustness and accuracy. This review provides a comprehensive overview of NIRS methodology including its principle, spectra pretreatments, spectral wavelength selection, outlier disposal, dataset division, regression methods, and model evaluation. More importantly, the applications of NIRS in the determination of analytical parameters, rheological parameters, and end product quality of wheat are summarized. Although NIRS showed great potential in the quantitative determination of analytical parameters, there are still challenges in model robustness and accuracy in determining rheological parameters and end product quality for wheat products. Future model development needs to incorporate larger databases, integrate different spectroscopic techniques, and introduce cutting-edge chemometrics methods. In addition, calibration based on external factors should be considered to improve the predicted results of the model. The NIRS application in micronutrients needs to be extended. Last, the idea of combining standard product sensory attributes and spectra for model development deserves further study.

Near infrared spectroscopy combined with chemometrics for quantitative analysis of corn oil in edible blend oil

In this study, near infrared (NIR) spectroscopy combined with chemometrics was used for the quantitative analysis of corn oil in binary to hexanary edible blend oil. Sesame oil, soybean oil, rice oil, sunflower oil and peanut oil were mixed with corn oil subsequently to form binary, ternary, quaternary, quinary and hexanary blend oil datasets. NIR spectra for the five order blend oil datasets were measured in a transmittance mode in the range of 12000-4000 cm^-1. Partial least square (PLS) was used to build models for the five datasets. Six spectral preprocessing methods and their combinations were investigated to improve the prediction performance. Furthermore, the optimal preprocessing-PLS models were further optimized by uninformative variable elimination (UVE), Monte Carlo uninformative variable elimination (MCUVE) and randomization test (RT) variable selection methods. The optimal models acquire root mean square error of prediction (RMSEP) of 1.7299, 2.2089, 2.3742, 2.5608 and 2.6858 for binary, ternary, quaternary, quinary and hexanary blend oil datasets, respectively. The determination coefficients of prediction set (R²_P) and residual predictive deviations (RPDs) for the five datasets are all above 0.93 and 3. Results show that the prediction accuracy is gradually decreased with the increasing of mixture order of blend oil. However, with proper spectral preprocessing and variable selection, the optimal models present good prediction accuracy even for the higher order blend oil. It demonstrates that NIR technology is feasible for determining the pure oil contents in binary to hexanary blend oil.

Effect of environment and field management strategies on phenolic acid profiles of hard red winter wheat genotypes

BACKGROUND

Integrated wheat management strategies can affect grain yield and flour end-use properties. However, the effect of integrated management and its interaction with environmental factors on the phenolic acid profiles of wheat has not been reported. The phenolic acid profile has become another parameter for the evaluation of wheat quality due to its potential health benefits.

RESULTS

Year × location × management and year × management × genotype interactions were significant for the total phenolic content (TPC) of wheat samples. The year × location × management × genotype interaction was significant for the concentration of trans-ferulic acid and several other phenolic acids. Field management practices with no fungicide application (e.g., farmer's practice, enhanced fertility) may lead to increased accumulation of phenolic compounds, especially for WB4458, which is more susceptible to fungi infection. However, this effect was also related to growing year and location. Higher soil nitrogen content at sowing also seems to affect the TPC and phenolic acid concentration positively.

CONCLUSION

Wheat phenolic acid profiles are affected by genotype, field management, environment, and their interactions. Intensified field management, in particular, may lead to decreased concentration of wheat phytochemicals. The level of naturally occurring nitrogen in the soil may also affect the accumulation of wheat phytochemicals. © 2021 Society of Chemical Industry.

Feasibility study on quantification and authentication of the cassava starch content in wheat flour for bread-making using NIR spectroscopy and digital images

This works proposed a feasibility study on NIR spectroscopy and chemometrics-assisted color histogram-based analytical systems (CACHAS) to determine and authenticate the cassava starch content in wheat flour. Prediction results of partial least squares (PLS) achieved coefficient of correlation (r_pred) of 0.977 and root mean square error of prediction (RMSEP) of 1.826 mg kg^-1 for the certified additive-free wheat flour, while r_pred of 0.995 and RMSEP of 1.004 mg kg^-1 were obtained for the commercial wheat flour containing chemical additives. Additionally, Data-Driven Soft Independent Modelling of Class Analogy (dd-SIMCA) presented similar predictive ability using NIR and CACHAS for the certified wheat flour, authenticating all target samples, besides correctly recognizing samples that could represent a fraud. No satisfactory results were obtained for the commercial wheat flour. Therefore, NIR spectroscopy is more useful to offer definitive quantitative and qualitative analysis, while CACHAS can only provide an alternative preliminary analysis.

Application of near-infrared spectroscopy for the nondestructive analysis of wheat flour: A review

The quality and safety of wheat flour are of public concern since they are related to the quality of flour products and human health. Therefore, efficient and convenient analytical techniques are needed for the quality and safety controls of wheat flour. Near-infrared (NIR) spectroscopy has become an ideal technique for assessing the quality and safety of wheat flour, as it is a rapid, efficient and nondestructive method. The application of NIR spectroscopy in the quality and safety analysis of wheat flour is addressed in this review. First, we briefly summarize the basic knowledge of NIR spectroscopy and chemometrics. Then, recent advances in the application of NIR spectroscopy for chemical composition, technological parameters, and safety analysis are presented. Finally, the potential of NIR spectroscopy is discussed. Combined with chemometric methods, NIR spectroscopy has been used to detect chemical composition, technological parameters, deoxynivalenol, adulterants and additives of wheat flour. Furthermore, NIR spectroscopy has shown great potential for the rapid and online analysis of the quality and safety of wheat flour. It is anticipated that the current review will serve as a reference for the future analysis of wheat flour by NIR spectroscopy to ensure the quality and safety of flour products.

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NIR spectroscopy is an ideal technique for analysis of wheat flour due to its rapid and nondestructive nature.

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Use of NIR spectroscopy for chemical composition, technological parameters, and safety analysis.

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Online and handheld NIR spectrometers for wheat flour detection are the future trends.

Effects of Different Pilot-Scale Milling Methods on Bioactive Components and End-Use Properties of Whole Wheat Flour

The health benefits from consumption of whole wheat products are widely recognized. This study investigated the effects of different pilot-scale milling methods on physicochemical properties, bioactive components, Chinese steamed bread (CSB), and Chinese leavened pancakes (CLP) qualities of whole wheat flour (WWF). The results indicated that WWF-1 from the reconstitution of brans processed by a hammer mill had the best CSB and CLP quality overall. WWF from entire grain grinding by a jet mill (65 Hz) contained the highest concentration of bioactive components including dietary fibers (DF) and phenolic acids. A finer particle size did not necessarily result in a higher content of phenolic antioxidants in WWF. DF contents and damaged starch were negatively correlated with CSB and CLP quality. Compromised reduced quality observed in CLP made from WWF indicated its potentially higher acceptance as a whole-grain product.

Analytical Methods for Lipid Oxidation and Antioxidant Capacity in Food Systems

Lipid oxidation is the most crucial quality parameter in foods. Many methods were developed to determine the level of oxidation and antioxidant activity. This review compares the methods used to determine lipid oxidation and antioxidant capacity in foods. Lipid oxidation methods developed are based on the direct or indirect measurement of produced primary or secondary oxidation substances. Peroxide values and conjugated diene methods determine the primary oxidative products of lipid oxidation and are commonly used for plant oils and high-fat products. 2-Thiobarbituric acid-reactive substances and chromatographic methods are used to determine the secondary products of oxidation and are suitable for meat and meat-based products. The fluorometric and sensory analyses are indirect methods. The antioxidant capacity of additives is determined indirectly using the lipid oxidation methods mentioned above or directly based on the free-radical scavenging activity of the antioxidant compounds. Each lipid oxidation and antioxidant capacity methods use different approaches, and one method cannot be used for all foods. Therefore, selecting proper methods for specific foods is essential for accurately evaluating lipid oxidation or antioxidant capacity.