Dynamic monitoring of fatty acid value in rice storage based on a portable near-infrared spectroscopy system

Rapid qualitative and quantitative analysis of benzo(b)fluoranthene (BbF) in shrimp using SERS-based sensor coupled with chemometric models

Benzo(b)fluoranthene (BbF), a polycyclic aromatic hydrocarbon (PAH), is a carcinogenic contaminant of concern in seafood. This study developed a simple, rapid, sensitive, and cost-effective surface-enhanced Raman scattering (SERS) sensor (AuNPs) coupled with chemometric models for detecting BbF in shrimp samples. Partial least squares (PLS) regression models were optimized using uninformative variable elimination (UVE), bootstrapping soft shrinkage (BOSS), and competitive adaptive reweighted sampling (CARS). Qualitative analysis was performed using principal component analysis (PCA), linear discriminant analysis (LDA), and k-nearest neighbors (KNN) to differentiate between BbF-contaminated and uncontaminated shrimp samples. The SERS-sensor exhibited excellent sensitivity (LOD = 0.12 ng/mL), repeatability (RSD = 6.21%), and anti-interference performance. CARS-PLS model demonstrated superior predictive ability (R2 = 0.9944), and qualitative analysis discriminated between contaminated and uncontaminated samples. The sensor's accuracy was validated using HPLC, demonstrating the ability of the SERS-sensor coupled with chemometrics to rapidly and reliably detect BbF in shrimp samples.

Determination of trace chelating carboxylic acids in rice by green extraction combined with liquid chromatography-mass spectrometry analysis and its application in the evaluation of old and new rice

RATIONALE

Accurate identification of old rice samples from new ones benefits their market circulation and consumers. However, the current detection methods are still not satisfactory because of their insufficient accuracy or (and) time-consuming process.

METHODS

Chelating carboxylic acids (CCAs) were selectively extracted from rice, by stirring with chelating resin and a dilute Na2CO3 solution. The green analytical chemistry guidelines for sample preparation were investigated by using the green chemistry calculator AGREE prep. The extractant was determined by liquid chromatography-mass spectrometry (LC/MS), and statistical analysis of the analytical data was carried out to evaluate the significance of the difference by ChiPlot.

RESULTS

The limit of quantitation for the CCAs is in the range of 1 to 50 ng/mL, with a reasonable reproducibility. The CCAs in 23 rice samples were determined within a wide concentration range from 0.03 to 1174 μg/g. Intriguingly, the content of citric acid, malonic acid, α-ketoglutaric acid and cis-aconite acid in new rice was each found to be distinctively higher than that in old rice by several times. Even mixtures of old and new rice were found to show much difference in the concentration of citric acid and malic acid.

CONCLUSION

A green analytical method has been developed for the simultaneous determination of CCAs by LC/MS analysis, and the identification of old rice samples from new ones was easily carried out according to their CCA content for the first time. The results indicated that the described method has powerful potential for the accurate identification of old rice samples from new ones.

Untargeted Lipidomics Reveal Quality Changes in High-Moisture Japonica Brown Rice at Different Storage Temperatures

Low temperatures are an effective way of delaying grain rancidity and deterioration. However, little is known about the difference in quality changes in high-moisture japonica brown rice at different storage temperatures. In this study, the storage quality changes in japonica brown rice with a 15.50% moisture content stored at 15 °C, 20 °C, and 25 °C were investigated. In addition, an untargeted lipidomics analysis coupled with gas chromatography and mass spectrometry (GC-MS) was applied to analyze the volatile compounds and metabolite changes in the high-moisture japonica brown rice. The results showed that storage at 15 °C could well maintain the color and aroma stability of the brown rice and delay the increase in fatty acid value (FAV). The lipidomics results showed that storage at 15 °C delayed glycerolipid and sphingolipid metabolism and reduced glycerophospholipid catabolism in the brown rice. The low-temperature environment regulated these three metabolic pathways to maintain higher contents of triglycerides (TG), phosphatidylserine (PS), abd phosphatidylethanolamine (PE), and lower contents of diglycerides (DG), OAcyl-(gamma-hydroxy) FA (OAHFA), ceramides (Cer), and glycosylceramides (Hex1Cer) in the high-moisture japonica brown rice, which maintained the storage stability of the brown rice. Our results proposed the cryoprotection mechanism of postharvest brown rice from the perspective of volatile compounds and metabolite changes, providing a foothold for the further exploration of low-temperature storage as a safe and efficient cryoprotectant in the grain storage field.

Portable NIR spectroscopy: the route to green analytical chemistry

There is a growing interest for cost-effective and nondestructive analytical techniques in both research and application fields. The growing approach by near-infrared spectroscopy (NIRs) pushes to develop handheld devices devoted to be easily applied for in situ determinations. Consequently, portable NIR spectrometers actually result definitively recognized as powerful instruments, able to perform nondestructive, online, or in situ analyses, and useful tools characterized by increasingly smaller size, lower cost, higher robustness, easy-to-use by operator, portable and with ergonomic profile. Chemometrics play a fundamental role to obtain useful and meaningful results from NIR spectra. In this review, portable NIRs applications, published in the period 2019-2022, have been selected to indicate starting references. These publications have been chosen among the many examples of the most recent applications to demonstrate the potential of this analytical approach which, not having the need for extraction processes or any other pre-treatment of the sample under examination, can be considered the "true green analytical chemistry" which allows the analysis where the sample to be characterized is located. In the case of industrial processes or plant or animal samples, it is even possible to follow the variation or evolution of fundamental parameters over time. Publications of specific applications in this field continuously appear in the literature, often in unfamiliar journal or in dedicated special issues. This review aims to give starting references, sometimes not easy to be found.

Impacts of Inherent Components and Nitrogen Fertilizer on Eating and Cooking Quality of Rice: A Review

With the continuous improvement of living standards, the preferences of consumers are shifting to rice varieties with high eating and cooking quality (ECQ). Milled rice is mainly composed of starch, protein, and oil, which constitute the physicochemical basis of rice taste quality. This review summarizes the relationship between rice ECQ and its intrinsic ingredients, and also briefly introduces the effects of nitrogen fertilizer management on rice ECQ. Rice varieties with higher AC usually have more long branches of amylopectin, which leach less when cooking, leading to higher hardness, lower stickinesss, and less panelist preference. High PC impedes starch pasting, and it may be hard for heat and moisture to enter the rice interior, ultimately resulting in worse rice eating quality. Rice with higher lipid content had a brighter luster and better eating quality, and starch lipids in rice have a greater impact on rice eating quality than non-starch lipids. The application of nitrogen fertilizer can enhance rice yield, but it also decreases the ECQ of rice. CRNF has been widely used in cereal crops such as maize, wheat, and rice as a novel, environmentally friendly, and effective fertilizer, and could increase rice quality to a certain extent compared with conventional urea. This review shows a benefit to finding more reasonable nitrogen fertilizer management that can be used to regulate the physical and chemical indicators of rice grains in production and to improve the taste quality of rice without affecting yield.

qFC6, a major gene for crude fat content and quality in rice

qFC6, a major quantitative trait locus for rice crude fat content, was fine mapped to be identical with Wx. FC6 negatively regulates crude fat content and rice quality. Starch, protein and lipids are the three major components in rice endosperm. The lipids content in rice influences both storage and quality. In this study, we identified a quantitative trait locus (QTL), qFC6, for crude fat (free lipids) content through association analysis and linkage analysis. Gene-based association analysis revealed that LOC_Os06g04200, also known as Wx, was the candidate gene for qFC6. Complementation and knockout transgenic lines revealed that Wx negatively regulates crude fat content. Lipid composition and content analysis by gas chromatography and taste evaluation analysis showed that FC6 positively influenced bound lipids content and negatively affected both free lipids content and taste. Besides, higher free lipids content rice varieties exhibit more lustrous appearance after cooking and by adding extra oil during cooking could improve rice luster and taste score, indicating that higher free lipids content may make rice more lustrous and delicious. Together, we cloned a QTL coordinating rice crude fat content and eating quality and assisted in uncovering the genetic basis of rice lipid content and in the improvement of rice eating quality.

A feasibility quantitative analysis of free fatty acids in polished rice by fourier transform near-infrared spectroscopy and chemometrics

Free fatty acids (FFAs) are an important indicator of the freshness and quality of rice. In this study, the vibration response of C-H chemical bonds (-CH₃ , -CH₂ , H-C = C-H) of FFAs in the near-infrared region was determined by analyzing the standard reagent. In addition, the spectral data of different physical forms of rice and chemometrics, such as partial least squares (PLS), synergy interval-PLS, and competitive adaptive reweighted sampling (CARS), were applied to develop an optimal regression model for rice FFAs determination. The performance of the FFAs model established by using the polished rice granule spectrum (PRG) combined with CARS was the best, the correlation coefficients of the calibration set and prediction set were 0.99 (root mean squared errors of the calibration = 2.00 mg/100 g) and 0.98 (root mean squared errors of the prediction = 3.21 mg/100 g), respectively, and the ratio of performance-to-deviation was 4.50. Compared with the rice powder spectral, the PRG spectral can better retain the information of FFAs. The result shows that NIRS can rapidly, non-destructively, and accurately detect FFAs in rice granules, which will help rice business and food regulatory authorities to establish an early warning mechanism of rice aging. PRACTICAL APPLICATION: Free fatty acids (FFAs) in rice are an important indicator for evaluating the freshness of rice, and their high responsiveness to the deterioration of rice quality. The real-time detection of FFAs in rice can timely adjust the parameters of the rice storage environment, which is very meaningful to ensure the quality of rice.

Determination of acid value during edible oil storage using a portable NIR spectroscopy system combined with variable selection algorithms based on an MPA-based strategy

BACKGROUND

The acid value is an important indicator for evaluating the quality of edible oil during storage. This study employs a portable near-infrared (NIR) spectroscopy system to determine the acid value during edible oil storage. Four MPA-based variable selection methods, namely competitive adaptive reweighted sampling (CARS), the variable iterative space shrinkage approach (VISSA), iteratively variable subset optimization (IVSO), and bootstrapping soft shrinkage (BOSS) were introduced to optimize the preprocessed NIR spectra. Support vector machine (SVM) models based on characteristic spectra obtained by different selection methods were then established to achieve quantitative detection of the acid value during edible oil storage.

RESULTS

The results revealed that, compared with the full-spectrum SVM model, the SVM models established by the characteristic wavelengths optimized by the variable selection methods based on the MPA strategy exhibit a significant improvement in complexity and generalization performance. Furthermore, compared with the CARS, VISSA, and IVSO methods, the BOSS method obtained the least number of characteristic wavelength variables, and the SVM model established based on the optimized features of this method exhibited the optimal prediction performance. The root mean square error of prediction (RMSEP) was 0.11 mg g-1, the coefficient of determination (Rp2) was 0.92 and the ratio performance deviation (RPD) was 2.82, respectively.

CONCLUSION

The overall results indicate that the variable selection methods based on the MPA strategy can select more targeted characteristic variables. This has good application prospects in NIR spectra feature optimization. © 2020 Society of Chemical Industry.

Determination of Fatty Acid Content of Rice during Storage Based on Feature Fusion of Olfactory Visualization Sensor Data and Near-Infrared Spectra

This study innovatively proposes a feature fusion technique to determine fatty acid content during rice storage. Firstly, a self-developed olfactory visualization sensor was used to capture the odor information of rice samples at different storage periods and a portable spectroscopy system was employed to collect the near-infrared (NIR) spectra during rice storage. Then, principal component analysis (PCA) was performed on the pre-processed olfactory visualization sensor data and the NIR spectra, and the number of the best principal components (PCs) based on the single technique model was optimized during the backpropagation neural network (BPNN) modeling. Finally, the optimal PCs were fused at the feature level, and a BPNN detection model based on the fusion feature was established to achieve rapid measurement of fatty acid content during rice storage. The experimental results showed that the best BPNN model based on the fusion feature had a good predictive performance where the correlation coefficient (R_P) was 0.9265, and the root mean square error (RMSEP) was 1.1005 mg/100 g. The overall results demonstrate that the detection accuracy and generalization performance of the feature fusion model are an improvement on the single-technique data model; and the results of this study can provide a new technical method for high-precision monitoring of grain storage quality.

QCM Sensor Arrays, Electroanalytical Techniques and NIR Spectroscopy Coupled to Multivariate Analysis for Quality Assessment of Food Products, Raw Materials, Ingredients and Foodborne Pathogen Detection: Challenges and Breakthroughs

Quality checks, assessments, and the assurance of food products, raw materials, and food ingredients is critically important to ensure the safeguard of foods of high quality for safety and public health. Nevertheless, quality checks, assessments, and the assurance of food products along distribution and supply chains is impacted by various challenges. For instance, the development of portable, sensitive, low-cost, and robust instrumentation that is capable of real-time, accurate, and sensitive analysis, quality checks, assessments, and the assurance of food products in the field and/or in the production line in a food manufacturing industry is a major technological and analytical challenge. Other significant challenges include analytical method development, method validation strategies, and the non-availability of reference materials and/or standards for emerging food contaminants. The simplicity, portability, non-invasive, non-destructive properties, and low-cost of NIR spectrometers, make them appealing and desirable instruments of choice for rapid quality checks, assessments and assurances of food products, raw materials, and ingredients. This review article surveys literature and examines current challenges and breakthroughs in quality checks and the assessment of a variety of food products, raw materials, and ingredients. Specifically, recent technological innovations and notable advances in quartz crystal microbalances (QCM), electroanalytical techniques, and near infrared (NIR) spectroscopic instrument development in the quality assessment of selected food products, and the analysis of food raw materials and ingredients for foodborne pathogen detection between January 2019 and July 2020 are highlighted. In addition, chemometric approaches and multivariate analyses of spectral data for NIR instrumental calibration and sample analyses for quality assessments and assurances of selected food products and electrochemical methods for foodborne pathogen detection are discussed. Moreover, this review provides insight into the future trajectory of innovative technological developments in QCM, electroanalytical techniques, NIR spectroscopy, and multivariate analyses relating to general applications for the quality assessment of food products.