Determination of insect infestation on stored rice by near infrared (NIR) spectroscopy

A one-class classification approach for authentication of specialty coffees by inductively coupled plasma mass spectroscopy (ICP-MS)

Due to the lucrative nature of specialty coffees, there have been instances of adulteration where low-cost materials are mixed in to increase the overall volume, resulting in illegal profit. A widely used and recommended approach to detect possible adulteration is the application of one-class classifiers (OCC), which only require information about the target class to build the models. Thus, this work aimed to identify adulterations in specialty coffees with low-quality coffee using multielement analysis determined by ICP-MS and to evaluate the performance of one-class classifiers (dd-SIMCA, OCRF, and OCPLS). Therefore, authentic specialty coffee samples were adulterated with low-quality coffee in 25 % to 75 % (w/w) proportions. Samples were subjected to acid decomposition for analysis by ICP-MS. OCPLS method presented the best performance to detect adulterations with low-quality coffee in specialty coffees, showing higher specificity (SPE = 100 %) and reliability rate (RLR = 94.3 %).

Prediction of cyanidin 3-rutinoside content in Michelia crassipes based on near-infrared spectroscopic techniques

Currently the determination of cyanidin 3-rutinoside content in plant petals usually requires chemical assays or high performance liquid chromatography (HPLC), which are time-consuming and laborious. In this study, we aimed to develop a low-cost, high-throughput method to predict cyanidin 3-rutinoside content, and developed a cyanidin 3-rutinoside prediction model using near-infrared (NIR) spectroscopy combined with partial least squares regression (PLSR). We collected spectral data from Michelia crassipes (Magnoliaceae) tepals and used five different preprocessing methods and four variable selection algorithms to calibrate the PLSR model to determine the best prediction model. The results showed that (1) the PLSR model built by combining the blockScale (BS) preprocessing method and the Significance multivariate correlation (sMC) algorithm performed the best; (2) The model has a reliable prediction ability, with a coefficient of determination (R2) of 0.72, a root mean square error (RMSE) of 1.04%, and a residual prediction deviation (RPD) of 2.06. The model can be effectively used to predict the cyanidin 3-rutinoside content of the perianth slices of M. crassipes, providing an efficient method for the rapid determination of cyanidin 3-rutinoside content.

Near-infrared spectroscopy bioprobe estimation of metabolites' responses to Pb2+ in Cladophora rupestris

Near-infrared (NIR) spectroscopy was used as a bioprobe to estimation chlorophyll, total sugar, proline, and metallothionein (MT) response to Pb2+ (0, 1, 2.5, 5, 7.5, 10, and 20 mg/l) in Cladophora rupestris. Spectra between 3775.94 and 12315.19 cm-1 were analysed using a partial least-squares regression (PLS). Principal component analysis (PCA) was performed by evaluating the distance between the C. rupestris samples treated with different concentrations of Pb2+. Results showed that 4000-7500 cm-1 of raw NIR spectra from C. rupestris was linked to carbohydrates and stretching of N-H, and the O-H overtone was associated with proline and MT. NIR spectroscopy technique coupled with PLS quantitative analysis model has high potential. Multiplicative scatter correction and standard normal variate combining first-order derivative with S-G smoothing filter are superior to the other models. The root mean square error of cross of chlorophyll, total sugar, proline, and MT was 0-1, indicating the accuracy of the model is high. The Rc2 values of chlorophyll, total soluble sugar, proline, and MT between measured and predicted values were higher than 0.99. PCA analysis indicate clear boundaries in the spatial distributions of seven Pb2+ concentrations, indicating that NIR spectroscopy has high potential as a tool assisting bioprobes for estimating metabolites' responses to Pb2+ in Cladophora rupestris.

Evaluating the Use of Vibrational Spectroscopy to Detect the Level of Adulteration of Cricket Powder in Plant Flours: The Effect of the Matrix

Edible insects have been recognised as an alternative food or feed ingredient due to their protein value for both humans and domestic animals. The objective of this study was to evaluate the ability of both near- (NIR) and mid-infrared (MIR) spectroscopy to identify and quantify the level of adulteration of cricket powder added into two plant proteins: chickpea and flaxseed meal flour. Cricket flour (CKF) was added to either commercial chickpea (CPF) or flaxseed meal flour (FxMF) at different ratios of 95:5% w/w, 90:10% w/w, 85:15% w/w, 80:20% w/w, 75:25% w/w, 70:30% w/w, 65:35% w/w, 60:40% w/w, or 50:50% w/w. The mixture samples were analysed using an attenuated total reflectance (ATR) MIR instrument and a Fourier transform (FT) NIR instrument. The partial least squares (PLS) cross-validation statistics based on the MIR spectra showed that the coefficient of determination (R2CV) and the standard error in cross-validation (SECV) were 0.94 and 6.68%, 0.91 and 8.04%, and 0.92 and 4.33% for the ALL, CPF vs. CKF, and FxMF vs. CKF mixtures, respectively. The results based on NIR showed that the cross-validation statistics R2CV and SECV were 0.95 and 3.16%, 0.98 and 1.74%, and 0.94 and 3.27% using all the samples analyzed together (ALL), the CPF vs. CKF mixture, and the FxMF vs. CKF mixture, respectively. The results of this study showed the effect of the matrix (type of flour) on the PLS-DA data in both the classification results and the PLS loadings used by the models. The different combination of flours (mixtures) showed differences in the absorbance values at specific wavenumbers in the NIR range that can be used to classify the presence of CKF. Research in this field is valuable in advancing the application of vibrational spectroscopy as routine tools in food analysis and quality control.

Slit dual-frequency ultrasound-assisted pulping of Lycium barbarum fresh fruit to improve the dissolution of polysaccharides and in situ real-time monitoring

In this study, the slit dual-frequency ultrasound-assisted pulping of fresh Lycium barbarum fruit was optimized to improve the dissolution of polysaccharides. The microscopic mechanism of polysaccharide dissolution was explored through establishing polysaccharides dissolution kinetics model and visualizing the multi-physical fields during ultrasonic process, and an in situ real-time monitoring model was established by the relationship between the chemical value and spectral information collected by near-infrared spectroscopy. The results showed that, under optimal conditions, treatment with ultrasound (28-33 kHz, 250 W, 30 min) not only significantly promoted the dissolution rate of polysaccharides in Lycium barbarum pulp (LBPPs, increased by 43.64 %, p < 0.01), reduced its molecular weight, but also improved the arabinose molar ratio, the uniformity of polysaccharide particles, and the antioxidant activity of LBPPs. Correlation analysis indicated that ultrasonic treatment is closely related to LBPPs content, particle size and scavenging capacity against superoxide anion radicals (ptotal sugar content < 0.01, pparticle size < 0.05 and psuperoxide anion scavenging < 0.05). Moreover, the in situ real-time monitoring model for the pulping process could quantitatively predict LBPPs dissolution rate and its superoxide anion radical scavenging capacity with good calibration and prediction performance (Rc = 0.9841, RMSECV = 0.0873, Rp = 0.9772, RMSEP = 0.0530; Rc = 0.9874, RMSECV = 0.1246, Rp = 0.9868, RMSEP = 0.0665). These results indicated that slit dual-frequency ultrasound has great potential in improving the quality of Lycium barbarum pulp, which may provide theoretical support for the industrial development of intelligent systems for polysaccharides preparation.

Analysis of the Composition of Different Instars of Tenebrio molitor Larvae using Near-Infrared Reflectance Spectroscopy for Prediction of Amino and Fatty Acid Content

Insects are a sustainable protein source for food and feed. The yellow mealworm (Tenebrio molitor L.) is a promising candidate for industrial insect rearing and was the focus of this study. This research revealed the diversity of Tenebrio molitor larvae in the varying larval instars in terms of the nutritional content. We hypothesized that water and protein are highest in the earlier instar, while fat content is very low but increases with larval development. Consequently, an earlier instar would be a good choice for harvest, since proteins and amino acids content decrease with larval development. Near-infrared reflectance spectroscopy (NIRS) was represented in this research as a tool for predicting the amino and fatty acid composition of mealworm larvae. Samples were scanned with a near-infrared spectrometer using wavelengths from 1100 to 2100 nm. The calibration for the prediction was developed with modified partial least squares (PLS) as the regression method. The coefficient for determining calibration (R²_C) and prediction (R²_P) were >0.82 and >0.86, with RPD values of >2.20 for 10 amino acids, resulting in a high prediction accuracy. The PLS models for glutamic acid, leucine, lysine and valine have to be improved. The prediction of six fatty acids was also possible with the coefficient of the determination of calibration (R²_C) and prediction (R²_P) > 0.77 and >0.66 with RPD values > 1.73. Only the prediction accuracy of palmitic acid was very weak, which was probably due to the narrow variation range. NIRS could help insect producers to analyze the nutritional composition of Tenebrio molitor larvae fast and easily in order to improve the larval feeding and composition for industrial mass rearing.

Current progress on innovative pest detection techniques for stored cereal grains and thereof powders

For stored grains and their powders, pest infestation has always been a knotty problem and thus comprises a serious threat to global food security. Obviously, timely, rapid and accurate pest detection methods are of extreme importance to protect grains from pest mouth. In facing the defects of traditional methods, such as visual inspection, grain flotation and pest trap, diverse innovative approaches progressed fast alternatively, either targeting pest itself or diagnosing pest-induced changes. The former includes machine vision, metabolite analysis, pest-specific protein techniques, molecular techniques, bioacoustics analysis, conductive roller mill, low-field nuclear magnetic resonance spectroscopy and imaging, while the latter consists of thermal imaging, near-infrared spectroscopy and hyperspectral imaging, impact acoustics analysis, soft X-ray imaging and tomography. The principle, operation procedure, pros and cons and application scenarios were discussed for each method. The results herein hope to promote the technical revolution of pest inspection in stored cereal grains and their powders.

The Impacts of Climate Change on the Potential Distribution of Plodia interpunctella (Hübner) (Lepidoptera: Pyralidae) in China

Simple Summary

Plodia interpunctella (Hübner) is an important grain storage pest in China that is distributed in almost every province and that has caused huge economic losses. In this study, we adjusted the biological parameters of the Indian grain borer and then used the CLIMEX model to predict the detailed potential distribution of P. interpunctella in China under current and future conditions. Under historical climatic conditions, the central, northeastern, and southern regions of China are highly suitable habitats for it. Because of temperature change in the future climate, suitable habitats will increase in the eastern part of Qinghai and will decrease in the mid-eastern, northeastern, and southeastern parts of China. This study provides important information for controlling the further spread of the Indian corn borer in China.

Abstract

The Indian meal moth Plodia interpunctella (Hübner) (Lepidoptera: Pyralidae) is a notorious stored-grain pest that can be found in most parts of China. The corpses, excretions, and other secretions of P. interpunctella larvae cause serious grain pollution, seriously affecting the nutritional and economic value of stored grain in China. To elucidate the potential distribution of P. interpunctella in China, we used the CLIMEX 4.0 model to project the potential distribution of the pest using historical climate data (1960–1990) and estimated future climate data (2030, 2050, and 2070). Under the historical climate situation, P. interpunctella was distributed in most areas of China, and its highly favorable habitats account for 48.14% of its total potential distribution. Because of temperature change in the future climate, suitable habitats will increase in the eastern part of Qinghai and will decrease in the mid-eastern, northeastern, and southeastern parts of China. Under these scenarios, the area of this pest’s highly favorable habitat will be reduced by 1.24 million km², and its proportion will decrease to about 28.48%. These predicted outcomes will help to distinguish the impact of climate change on the potential distribution of P. interpunctella, thereby providing important information to design early forecasting and strategies to prevent pest harm to stored grain.

Determination of Moisture and Protein Content in Living Mealworm Larvae (Tenebrio molitor L.) Using Near-Infrared Reflectance Spectroscopy (NIRS)

Yellow mealworm larvae (Tenebrio molitor L.) are a sustainable source of protein for food and feed. This study represents a new approach in analyzing changes in the nutritional composition of mealworm larvae using near-infrared reflectance spectroscopy (NIRS) combined with multivariate analysis. The moisture and protein content of living larvae were scanned with a near-infrared spectrometer using wavelengths from 1100 to 2100 nm. Different feeding groups with varying moisture sources and amount and the difference between low (50%) and high (75%) humidity were tested, and the influence on larval moisture and protein content was measured. A calibration was developed, with modified partial least squares as the regression method. The NIR spectra were influenced by the moisture and protein content of the larvae, because the absorbance values of the larval groups differed greatly. The coefficient of the determination of calibration (R2c) and prediction (R2p) were over 0.98 for moisture and over 0.94 for protein content. The moisture source and content also had a significant influence on the weight gain of the larvae. Consequently, significant differences in protein content could be determined, depending on the water supply available. With respect to wet weight, the larvae moisture content varied from 60 to 74% and protein content from 16 to 24%. This investigation revealed that with non-invasive NIRS online monitoring, the composition of insects can be continuously recorded and evaluated so that specific feeding can be carried out in the course of larval development and composition.

Analysis of wheat flour-insect powder mixtures based on their near infrared spectra

Insects are gaining more and more space in food and feed sectors, creating an intense scientific interest towards insects as food ingredients. Several papers deal with cereal-based products complemented by insect powder in the past few years. However, adulteration and quality control of such products present some hot topics for researchers, e.g., how can we justify the amounts and/or species of the insects used in the given products? Our paper aims to answer such questions by analysing seven edible insect powders of different species independently. The mixtures with wheat flour were analysed using near infrared spectroscopy and chemometric methods. Not only powders of different species were clearly differentiated, but also mixtures created by different amounts of wheat flour. Prediction of insect content showed 0.65% cross-validated error. The proposed methodology gives an excellent tool for quality control of insect-based cereal food products.

Multispectral Wavebands Selection for the Detection of Potential Foreign Materials in Fresh-Cut Vegetables

Ensuring the quality of fresh-cut vegetables is the greatest challenge for the food industry and is equally as important to consumers (and their health). Several investigations have proven the necessity of advanced technology for detecting foreign materials (FMs) in fresh-cut vegetables. In this study, the possibility of using near infrared spectral analysis as a potential technique was investigated to identify various types of FMs in seven common fresh-cut vegetables by selecting important wavebands. Various waveband selection methods, such as the weighted regression coefficient (WRC), variable importance in projection (VIP), sequential feature selection (SFS), successive projection algorithm (SPA), and interval PLS (iPLS), were used to investigate the optimal multispectral wavebands to classify the FMs and vegetables. The application of selected wavebands was further tested using NIR imaging, and the results showed good potentiality by identifying 99 out of 107 FMs. The results indicate the high applicability of the multispectral NIR imaging technique to detect FMs in fresh-cut vegetables for industrial application.

Application of spectral features for separating homochromatic foreign matter from mixed congee

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A method that can separate homochromatic FM in mixed congee was proposed.

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Spectral features of FM and mixed congee were extracted to build recognition model.

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The SVM model achieved high identification rates (99.17%) for homochromatic FM.

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The proposed method is better than computer vision in separating homochromatic FM.

Foreign matter (FM) in mixed congee not only reduces the quality of the congee but may also harm consumers. However, the common computer vision methods with poor recognition ability for the homochromatic FM. This study used hyperspectral reflectance images with the pattern recognition model to detect homochromatic FM on the mixed congee surface. First, spectral features corresponding to homochromatic FM and background were extracted from hyperspectral images. Then, based on the optimal spectral preprocessing method, LDA, K-nearest neighbor, backpropagation artificial neural network, and support vector machine (SVM) were used to classify the spectral features. The results revealed that the SVM model input with raw spectra principal components exhibited optimal identification rates of 99.17%. Finally, most of the pixels for homochromatic FM were classified correctly by using the SVM model. To summarized, hyperspectral images combined with pattern recognition are an effective method for recognizing homochromatic FM in mixed congee.

Research advance in gas detection of volatile organic compounds released in rice quality deterioration process

Rice quality deterioration will cause grievous waste of stored grain and various food safety problems. Gas detection of volatile organic compounds (VOCs) produced by deterioration is a nondestructive detection method to judge rice quality and alleviate rice spoilage. This review discussed the research advance of VOCs detection in terms of nondestructive detection methods of rice quality deterioration, applications of VOCs in grain detection, inspection of characteristic gas produced during rice spoilage, rice deterioration prevention and control, and detection of VOCs released by rice mildew and insect attack. According to the main causes of rice quality deterioration and major sources of VOCs with off-odor generated during rice storage, deterioration can be divided into mold and insect infection. The results of literature manifested that researches mainly focused on the infection of Aspergillus in the mildew process and the attack of certain pests in recent years, thus the research scope was limited. In this paper, the gas detection methods combined with the chemometrics to qualitatively analyze the VOCs, as well as the correlation with the number of colonies and insects were further studied based on the common dominant strains during rice mildew, that is, Aspergillus and Penicillium fungi, and the common pests during storage, that is, Sitophilus oryzae and Rhyzopertha dominica. Furthermore, this paper pointed out that the quantitative determination of characteristic VOCs, the numeration relationship between VOCs and the degree of mildew and insect infestation, the further expansion of detection range, and the application of degraded rice should be the spotlight of future research.

An Analytical Method Based on Electrochemical Sensor for the Assessment of Insect Infestation in Flour

Uric acid is an important indicator of the insect infestation assessment in flour. In this work, we propose a method for uric acid detection based on voltammetry. This technique is particularly considered for the physicochemical properties of flour and contains a simple pretreatment process to rapidly achieve extraction and adsorption of uric acid in flour. To achieve specific recognition of uric acid, graphene and poly(3,4-ethylenedioxythiophene) (PEDOT) were used for the adsorption and concentration of uric acid in flour. The adsorbed mixture was immobilized on the surface of a screen-printed electrode for highly sensitive detection of the uric acid. The results showed that electrocatalytic oxidation of uric acid could be achieved after adsorption by graphene and PEDOT. This electrocatalytic reaction allows its oxidation peak to be distinguished from those of other substances that commonly possess electrochemical activity. This voltammetry-based detection method is a portable and disposable analytical method. Because it is simple to operate, requires no professional training, and is inexpensive, it is a field analysis method that can be promoted.

Infrared spectroscopy and forensic entomology: Can this union work? A literature review

For more than two decades, infrared spectroscopy techniques combined with multivariate analysis have been efficiently applied in several entomological fields, such as Taxonomy and Toxicology. However, little is known about its use and applicability in Forensic entomology (FE) field, with vibrational techniques such as Near-infrared spectroscopy (NIRS) and Medium-infrared spectroscopy (MIRS) underutilized in forensic sciences. Thus, this work describes the potential of NIRS, MIRS, and other spectroscopic methodologies, for entomological analysis in FE, as well as discusses its future uses for criminal or civil investigations. After a thorough research on scientific journals database, a total of 33 publications were found in scientific journals, with direct or indirect application to FE, including experimental applications of NIRS and MIRS in taxonomic discrimination of species, larval age prediction, detection of toxic substances in insects from environments or crime scenes, and detection of internal or external infestations by live or dead insects in stored products. Besides, NIRS and MIRS combined with multivariate analysis were efficient, inexpensive, fast, and non-destructive analytical tools. However, more than 51% of the spectroscopic publications are concentrated in the stored products field, and so we discuss the need for expansion and more direct application in other FE areas. We hope the number of articles continues to increase, and as NIRS and MIRS technology progress, they advance in forensic research and routine use.

Effects of Different Infra-Red Irradiations on the Survival of Granary Weevil Sitophilus granarius: Bioefficacy and Sustainability

Sitophilus granarius (L.) is an important pest of stored grain worldwide. In recent years, sustainable methods against it have received attention as grain stock protective means. Our aim was to obtain information about the efficacy of infrared irradiation (IR) against S. granarius in laboratory conditions. The change in adult-activity and median lethal dose (LD50) triggered by IR in S. granarius was examined. The insecticidal efficacy in the infested grains was also analyzed at 12, 24, 48, and 72h following exposure to IR (250W), and the progeny-production was assessed 45 days upon the treatment. Based on our findings, total mortality ensued in a grain stock of 50 g at 412 s and a for 100 g grain at 256 s. A significant increase in S. granarius mortality could be observed in the higher grain weight regime, which can be accounted for by the higher heat-absorbance of objects with higher weight. The activity of pests immediately after the beginning of IR increased and subsequently became moderated. The observation of activity-peak brought about by irradiation contribute to the optimization of chemical intervention. This treatment could provide an effective and sustainable technique in integrated pest management.

Multi-variable selection strategy based on near-infrared spectra for the rapid description of dianhong black tea quality

The main objectives of the study are to understand and explore critical feature wavelengths of the obtained near-infrared (NIR) data relating to dianhong black tea quality categories, we propose a multi-variable selection strategy based on the variable space optimization from big to small which is the kernel idea of a variable combination of the improved genetic algorithm (IGA) and particle swarm optimization (PSO) in this study. A rapid description based on the NIR technology is implemented to assess black tea tenderness and rankings. First, 700 standard samples from dianhong black tea of seven quality classes are scanned using a NIR system. The raw spectra acquired are preprocessed by Savitzky-Golay (SG) filtering coupled with standard normal variate transformation (SNV). Then, the multi-variable selection algorithm (IGA-PSO) is applied to compare with the single method (the IGA and PSO) and search the optimal characteristic wavelengths. Finally, the identification models are developed using a decision tree (DT), partial least-squares discriminant analysis (PLS-DA), and support vector machine (SVM) based on different kernel functions combined with the effective features from the above variables screening paths for the discrimination of black tea quality. The results show that the IGA-PSO-SVM model with a radial basis function achieves the best predictive results with the correct discriminant rate (CDR) of 95.28% based on selected four characteristic variables in the prediction process. The overall results demonstrate that NIR combined with a multi-variable selection method can constitute a potential tool to understand the most important features involved in the evaluation of dianhong black tea quality helping the instrument manufacturers to achieve the development of low-cost and handheld NIR sensors.

Authentication of Rice (Oryza sativa L.) Using Near Infrared Spectroscopy Combined with Different Chemometric Classification Strategies

Rice is a staple food in Vietnam, and the concern about rice is much greater than that for other foods. Preventing fraud against this product has become increasingly important in order to protect producers and consumers from possible economic losses. The possible adulteration of this product is done by mixing, or even replacing, high-quality rice with cheaper rice. This highlights the need for analytical methodologies suitable for its authentication. Given this scenario, the present work aims at testing a rapid and non-destructive approach to detect adulterated rice samples. To fulfill this purpose, 200 rice samples (72 authentic and 128 adulterated samples) were analyzed by near infrared (NIR) spectroscopy coupled, with partial least squares-discriminant analysis (PLS-DA) and soft independent modeling of class analogies (SIMCA). The two approaches provided different results; while PLS-DA analysis was a suitable approach for the purpose of the work, SIMCA was unable to solve the investigated problem. The PLS-DA approach provided satisfactory results in discriminating authentic and adulterated samples (both 5% and 10% counterfeits). Focusing on authentic and 10%-adulterated samples, the accuracy of the approach was even better (with a total classification rate of 82.6% and 82.4%, for authentic and adulterated samples, respectively).

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.

Chemometric Strategies for Spectroscopy-Based Food Authentication

In the last decades, spectroscopic techniques have played an increasingly crucial role in analytical chemistry, due to the numerous advantages they offer. Several of these techniques (e.g., Near-InfraRed—NIR—or Fourier Transform InfraRed—FT-IR—spectroscopy) are considered particularly valuable because, by means of suitable equipment, they enable a fast and non-destructive sample characterization. This aspect, together with the possibility of easily developing devices for on- and in-line applications, has recently favored the diffusion of such approaches especially in the context of foodstuff quality control. Nevertheless, the complex nature of the signal yielded by spectroscopy instrumentation (regardless of the spectral range investigated) inevitably calls for the use of multivariate chemometric strategies for its accurate assessment and interpretation. This review aims at providing a comprehensive overview of some of the chemometric tools most commonly exploited for spectroscopy-based foodstuff analysis and authentication. More in detail, three different scenarios will be surveyed here: data exploration, calibration and classification. The main methodologies suited to addressing each one of these different tasks will be outlined and examples illustrating their use will be provided alongside their description.

Cognitive spectroscopy for evaluating Chinese black tea grades (Camellia sinensis): near-infrared spectroscopy and evolutionary algorithms

BACKGROUND

Grading represents an essential criterion for the quality assurance of black tea. The main objectives of the study were to develop a highly robust model for Chinese black tea of seven grades based on cognitive spectroscopy.

RESULTS

Cognitive spectroscopy was proposed to combine near-infrared spectroscopy (NIRS) with machine learning and evolutionary algorithms, selected feature information from complex spectral data and show the best results without human intervention. The NIRS measuring system was used to obtain the spectra of Chinese black tea samples of seven grades. The spectra acquired were preprocessed by standard normal variate transformation (SNV), multiplicative scatter correction (MSC) and minimum/maximum normalization (MIN/MAX), and the optimal pretreating method was implemented using principal component analysis combined with linear discriminant analysis algorithm. Three feature selection evolutionary algorithms, which were a genetic algorithm (GA), simulated annealing (SA) and particle swarm optimization (PSO), were compared to search the best preprocessed characteristic wavelengths. Cognitive models of Chinese black tea ranks were constructed using extreme learning machine (ELM), K-nearest neighbor (KNN) and support vector machine (SVM) methods based on the selected characteristic variables. Experimental results revealed that the PSO-SVM model showed the best predictive performance with the correlation coefficients of prediction set (R_p ) of 0.9838, the root mean square error of prediction (RMSEP) of 0.0246, and the correct discriminant rate (CDR) of 98.70%. The extracted feature wavelengths were only occupying 0.18% of the origin.

CONCLUSION

The overall results demonstrated that cognitive spectroscopy could be utilized as a rapid strategy to identify Chinese black tea grades. © 2020 Society of Chemical Industry.

Potential biomonitoring of atmospheric carbon dioxide in Coffea arabica leaves using near-infrared spectroscopy and partial least squares discriminant analysis

The potencial of Coffea arabica leaves as bioindicators of atmospheric carbon dioxide (CO₂) was evaluated in a free-air carbon dioxide enrichment (FACE) experiment by using near-infrared reflectance (NIR) spectroscopy for direct analysis and partial least squares discriminant analysis (PLS-DA). A supervised classification model was built and validated from the spectra of coffee leaves grown under elevated and current CO₂ levels. PLS-DA allowed correct test set classification of 92% of the elevated-CO₂ level leaves and 100% of the current-CO₂ level leaves. The spectral bands accounting for the discrimination of the elevated-CO₂ leaves were at 1657 and 1698 nm, as indicated by the variable importance in the projection (VIP) score together with the regression coefficients. Seven months after suspension of enriched CO₂, returning to current-CO₂ levels, new spectral measurements were made and subjected to PLS-DA analysis. The predictive model correctly classified all leaves as grown under current-CO₂ levels. The fingerprints suggest that after suspension of elevated-CO₂, the spectral changes observed previously disappeared. The recovery could be triggered by two reasons: the relief of the stress stimulus or the perception of a return of favorable conditions. In addition, the results demonstrate that NIR spectroscopy can provide a rapid, nondestructive, and environmentally friendly method for biomonitoring leaves suffering environmental modification. Finally, C. arabica leaves associated with NIR and mathematical models have the potential to become a good biomonitoring system.

Noble Metal Nanoparticles Applications: Recent Trends in Food Control

Scientific research in the nanomaterials field is constantly evolving, making it possible to develop new materials and above all to find new applications. Therefore, nanoparticles (NPs) are suitable for different applications: nanomedicine, drug delivery, sensors, optoelectronics and food control. This review explores the recent trend in food control of using noble metallic nanoparticles as determination tools. Two major uses of NPs in food control have been found: the determination of contaminants and bioactive compounds. Applications were found for the determination of mycotoxins, pesticides, drug residues, allergens, probable carcinogenic compounds, bacteria, amino acids, gluten and antioxidants. The new developed methods are competitive for their use in food control, demonstrated by their validation and application to real samples.