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Improvement of the sense of gain as an internal driving force is the key factor to improve the training quality of graduate students in food science. Utilizing Jiangsu University graduate students majoring in food science as research samples, this study analyzed the present situation of the “sense of gain” demand. We analyzed the reasonable appeals of graduate students during their study based on fully respecting and advocating students’ right of speech, listened to their opinions and suggestions on higher education, analyzed the main contradictions, and further put forward a series of countermeasures. For improving the graduate students’ sense of gain during the period of study, it is necessary to improve the training quality from the following five aspects: constructing high-quality courses, cultivating people’s responsibilities, implementing “soft elimination” of training links, carrying out diversified extracurricular activities, and developing comprehensive quality. This research is significant in improving the training quality of food science graduate students.

Heavy metals accumulating in the human body produce physiological toxicity by interfering with the transport of human proteins and enzymes. Heavy metals detection is significant for food safety ass...

Mycotoxin contamination is a severe threat to global food security, thus fast and effective detection of mycotoxins is of great significance. Herein, mesoporous silica surface loaded gold nanocomposites (MSN-Rh6G-AuNPs) were prepared as surface-enhanced Raman scattering (SERS) substrate, and the SERS aptasensor (MSN-Rh6G-AuNPs@apt) was further obtained by aptamer functionalization which can realize the quantitative and sensitive detection of zearalenone (ZEN). The small nanogaps between AuNPs made MSN-Rh6G-AuNPs present strong SERS performance under excitation light irradiation, while the aptamer performed the functions of ZEN recognition and Raman signal masking. The acquired results revealed that the SERS intensity at 1508 cm

Background According to the World Health Organization, two billion people will attain the age of 60 years or more by 2050. Ageing is a major risk factor for a number of neurodegenerative disorders, which currently possess challenge to the global health status, carrying economic and social consequences. Therefore, attention has been dedicated towards the development of neuroprotective agents derived from natural sources. Honeybee products, such as honey, bee pollen, bee bread, propolis, royal jelly, beeswax, and bee venom have been used for therapeutic purposes since ancient times in Egypt, Greece, and China. Despite the emergence of modern medicine, bee products remain clinically relevant owing to their potential as anti-inflammatory, anti-oxidant, and neuroprotective agents. Scope and approach This review demonstrates the potential of bee products against neurological disorders in the light of the current literature. Key findings and conclusions Bee products and individual isolated components have enormous therapeutic potential for multiple neurological disorders. The different studies show overall neuroprotective and nerve-tonic characteristics of bee products, mainly due to their anti-oxidant, anti-inflammatory and anti-apoptotic features. However, some limitations such as allergic reactions and the cytotoxic effect of some bee products warrant a special care in its development as drug leads in future studies.

Morphine is the most representative characteristic alkaloid existed in the extract solution of poppy shell. The analysis of morphine can be used to judge whether poppy shell is illegally added to the hot pot soup base. A sensitive, environmental-friendly carbon quantum dots (C-Dots)–based fluorescence immunoassay (C-Dots-FLISA) was developed to detect morphine (MOP). Novel water-soluble C-Dots were synthesized using a one-step hydrothermal reaction, with citric acid serving as the carbon source and ethylene diamine acting as the nitrogen source. Moreover, the C-Dots displayed blue fluorescence with an emission peak at 440 nm (350 nm excitation), which also showed good stability. C-Dots which contained amine were used to conjugate with anti-morphine antibody by glutaraldehyde as coupling reagents. The anti-morphine antibody–labeled C-Dots (Abs-labeled C-Dots) were characterized by fluorescence spectrum, transmission electron microscopy (TEM), and gel electrophoresis. C-Dots-FLISA was developed and applied to quantitative detection of morphine. Under the optimal conditions, the linear range spanned from 3.2 × 10−4 to 10 mg/L (R2 = 0.992), and the detection limit was 3 × 10−4 mg/L. These results demonstrated that the developed C-Dots-FLISA could be applied as a sensitive and convenient tool for rapid detection of morphine.

To establish a rapid, convenient, and low‐cost method to assess the quality of Atlantic salmon, we analyzed the impedance between 10–1 and 105 Hz for Atlantic salmon/rainbow trout, chilled/frozen‐thawed salmon, and fresh/stale salmon. We combined chemometrics with impedance properties to create a multi‐quality index for Atlantic salmon. The accuracy of all three models established can reach 100% in distinguishing Atlantic salmon from rainbow trout and distinguishing chilled salmon from frozen‐thawed salmon. We applied a partial least squares method to create a quantitative prediction model of bioimpedance spectroscopy and the value of total volatile basic nitrogen. The correlation coefficients of the training and test sets were 0.9447 and 0.9387. Our results showed that the combination of impedance properties and chemometrics was a simple and effective application to evaluate Atlantic salmon quality., We established a multi‐quality rapid detection method to detect different quality Atlantic salmon by combining chemometrics with impedance properties. And the characteristics of bioimpedance spectra of salmon with different qualities were analyzed theoretically.

Cancer is one of the major causes of death globally. Currently, various methods are used to treat cancer, including radiotherapy, surgery, and chemotherapy, all of which have serious adverse effects. A healthy lifestyle, especially a nutritional diet, plays a critical role in the treatment and prevention of many disorders, including cancer. The above notion, plus the trend in going back to nature, encourages consumers and the food industry to invest more in food products and to find potential candidates that can maintain human health. One of these agents, and a very notable food agent, is royal jelly (RJ), known to be produced by the hypopharyngeal and mandibular salivary glands of young nurse honeybees. RJ contains bioactive substances, such as carbohydrates, protein, lipids, peptides, mineral salts and polyphenols which contribute to the appreciated biological and pharmacological activities. Antioxidant, anticancer, anti-inflammatory, antidiabetic, and antibacterial impacts are among the well-recognized benefits. The combination of RJ or its constituents with anticancer drugs has synergistic effects on cancer disorders, enhancing the drug’s effectiveness or reducing its side effects. The purpose of the present review is to emphasize the possible interactions between chemotherapy and RJ, or its components, in treating cancer illnesses.

Zearalenone (ZEN) is a universal mycotoxin contaminant in corn and its products. A surface-enhanced Raman scattering (SERS) based test strip was proposed for the detection of ZEN, which had the advantages of simplicity, rapidity, and high sensitivity. Core-shell Au@AgNPs with embedded reporter molecules (4-MBA) were synthesized as SERS nanoprobe, which exhibited excellent SERS signals and high stability. The detection range of ZEN for corn samples was 10-1000 μg/kg with the limit of detection (LOD) of 3.6 μg/kg, which is far below the recommended tolerable level (60 μg/kg). More importantly, the SERS method was verified by HPLC in the application on corn samples contaminated with ZEN, and the coincidence rates were in the range of 86.06%-111.23%, suggesting a high accuracy of the SERS assay. Therefore, the SERS-based test strip with an analysis time of less than 15 min is a promising tool for accurate and rapid detection of ZEN-field contamination.

Cadmium (Cd) causes pervasive harm on human health as a poisonous heavy metal. This study proposed a surface-enhanced Raman spectroscopy (SERS) approach using sodium alginate (SA) as green reductant in combination with edge enrichment and chemometrics to build label-free Cd quantitative models. The silver nanoparticles synthesized by SA had good dispersion and enhancement factor (3.48 × 10

Zearalenone is a contaminant in food and feed products which are hazardous to humans and animals. This study explored the feasibility of the Raman rapid screening technique for zearalenone in contaminated maize. For representative Raman spectra acquisition, the ground maize samples were collected by extended sample area to avoid the adverse effect of heterogeneous component. Regression models were built with partial least squares (PLS) and compared with those built with other variable selection algorithms such as synergy interval PLS (siPLS), ant colony optimization PLS (ACO-PLS) and siPLS-ACO. SiPLS-ACO algorithm was superior to others in terms of predictive power performance for zearalenone analysis. The best model based on siPLS-ACO achieved coefficients of correlation (Rp) of 0.9260 and RMSEP of 87.9132 μg/kg in the prediction set, respectively. Raman spectroscopy combined multivariate calibration showed promising results for the rapid screening large numbers of zearalenone maize contaminations in bulk quantities without sample-extraction steps.

The feasibility of rapid identification of Lactobacillus species using near-infrared spectral features coupled with chemometrics was investigated. First, bacterial colonies of 11 Lactobacillus strains covering four species ( Lactobacillus casei, Lactobacillus reuteri, Lactobacillus brevis, and Lactobacillus fermentum) were cultured using the spread-plate technique. Near-infrared spectra data of the Lactobacillus species were collected directly from the bacterial colonies. Second, 10 wavenumbers were selected from the near-infrared spectra data using uninformative variables elimination and genetic algorithm, and calibration models based on the 10 selected wavenumbers were built using least squares support vector machine. The identification rates for the prediction set and validation set were 89.04 and 85%, respectively. Third, chemical groups of the Lactobacillus cells contributing to the identification of the Lactobacillus strains were identified using mid infrared. The results of mid infrared data analysis indicated that 9 chemical groups could be considered characteristics for categorizing the 11 Lactobacillus strains. The relationship between the 10 selected wavenumbers and identified chemical groups was identified, which supported the satisfactory performance of the least squares support vector machine calibration model. This study demonstrated that near-infrared spectral features of bacterial colonies could be used for Lactobacillus typing at the strain level.

Background The study reports a portable near infrared (NIR) spectroscopy system coupled with chemometric algorithms for prediction of tea polyphenols and amino acids in order to index matcha tea quality. Results Spectral data were preprocessed by standard normal variate (SNV), mean center (MC) and first-order derivative (1st D) tests. The data were then subjected to full spectral partial least squares (PLS) and four variable selection algorithms, such as random frog partial least square (RF-PLS), synergy interval partial least square (Si-PLS), genetic algorithm-partial least square (GA-PLS) and competitive adaptive reweighted sampling partial least square (CARS-PLS). RF-PLS was established and identified as the optimum model based on the values of the correlation coefficients of prediction (RP ), root mean square error of prediction (RMSEP) and residual predictive deviation (RPD), which were 0.8625, 0.82% and 2.13, and 0.9662, 0.14% and 3.83, respectively, for tea polyphenols and amino acids. The content range of tea polyphenols and amino acids in matcha tea samples was 8.51-14.58% and 2.10-3.75%, respectively. The quality of matcha tea was successfully classified with an accuracy rate of 83.33% as qualified, unqualified and excellent grade. Conclusion The proposed method can be used as a rapid, accurate and non-destructive platform to classify various matcha tea samples based on the ratio of tea polyphenols to amino acids. © 2019 Society of Chemical Industry.

Meat is highly perishable and poses health threats when its quality and safety is unmonitored. Chemical methods of quality and safety determination are expensive, time-consuming and lack real-time ...

This study attempted to detect the total theaflavins content in black tea using a portable electronic tongue integrated with chemometric algorithms. Glassy carbon as a working electrode was used to collect the cyclic voltammetry current signals from the black tea samples. The synergy interval partial least square with competitive adaptive reweighted sampling (Si-CARS-PLS) was attempted to optimize and select the most informative current signal variables at special potentials for the prediction of the total theaflavins content in black tea. Models were optimized via cross validation. Compared with other characteristic variables selection methods, Si-CARS-PLS showed the best performance, employing only 13 variables (0.23% of the total variables), to achieve Rp = 0.8302 and RMSEP = 0.257 in the prediction set. The portable electronic tongue based on glassy carbon electrode and cyclic voltammetry combined with variable selection algorithm Si-CARS-PLS, proved a promising, rapid and cost-effective method to measure the total theaflavins content in black tea.

Bee pollen is a natural cocktail of floral nectar, flower pollen, enzymes, and salivary secretions produced by honeybees. Bee pollen is one of the bee products most enriched in proteins, polysaccharides, polyphenols, lipids, minerals, and vitamins. It has a significant health and medicinal impact and provides protection against many diseases, including diabetes, cancer, infectious, and cardiovascular. Bee pollen is commonly promoted as a cost-effective functional food. In particular, bee pollen has been applied in clinical trials for allergies and prostate illnesses, with a few investigations on cancer and skin problems. However, it is involved in several patents and health recipes to combat chronic health problems. This review aimed to highlight the clinical trials and patents involving bee pollen for different cases and to present the role of bee pollen as a supplementary food and a potential product in cosmetic applications.

It is crucial for the efficacy of the apple storage to apply methods like electronic nose systems for detection and prediction of spoilage or infection by Penicillium expansum. Based on the acquisition of electronic nose signals, selected sensitive feature sensors of spoilage apple and all sensors were analyzed and compared by the recognition effect. Principal component analysis (PCA), principle component analysis-discriminant analysis (PCA-DA), linear discriminant analysis (LDA), partial least squares discriminate analysis (PLS-DA) and K-nearest neighbor (KNN) were used to establish the classification model of apple with different degrees of corruption. PCA-DA has the best prediction, the accuracy of training set and prediction set was 100% and 97.22%, respectively. synergy interval (SI), genetic algorithm (GA) and competitive adaptive reweighted sampling (CARS) are three selection methods used to accurately and quickly extract appropriate feature variables, while constructing a PLS model to predict plaque area. Among them, the PLS model with unique variables was optimized by CARS method, and the best prediction result of the area of the rotten apple was obtained. The best results are as follows: Rc = 0.953, root mean square error of calibration (RMSEC) = 1.28, Rp = 0.972, root mean square error of prediction (RMSEP) = 1.01. The results demonstrated that the electronic nose has a potential application in the classification of rotten apples and the quantitative detection of spoilage area.

Apple is the most widely planted fruit in the world and is popular in consumers because of its rich nutritional value. In this study, the portable near‐infrared (NIR) transmittance spectroscopy coupled with temperature compensation and chemometric algorithms was applied to detect the storage quality of apples. The postharvest quality of apples including soluble solids content (SSC), vitamin C (VC), titratable acid (TA), and firmness was evaluated, and the portable spectrometer was used to obtain near‐infrared transmittance spectra of apples in the wavelength range of 590–1,200 nm. Mixed temperature compensation method (MTC) was used to reduce the influence of temperature on the models and to improve the adaptability of the models. Then, variable selection methods, such as uninformative variable elimination (UVE), competitive adaptive reweighted sampling (CARS), and successive projections algorithm (SPA), were developed to improve the performance of the models by determining characteristic variables and reducing redundancy. Comparing the full spectral models with the models established on variables selected by different variable selection methods, the CARS combined with partial least squares (PLS) showed the best performance with prediction correlation coefficient (R p) and residual predictive deviation (RPD) values of 0.9236, 2.604 for SSC; 0.8684, 2.002 for TA; 0.8922, 2.087 for VC; and 0.8207, 1.992 for firmness, respectively. Results showed that NIR transmittance spectroscopy was feasible to detect postharvest quality of apples during storage., Near‐infrared transmittance spectroscopy coupled with temperature compensation and chemometric algorithms was applied to detect the storage quality of apples. Results showed that near‐infrared transmittance spectroscopy was feasible to detect postharvest quality of apples during storage.

Lead ion (Pb2+) is a main heavy metal in food that causes heavy teratogenicity and carcinogenicity. In this study, a rapid and sensitive SERS method for detecting Pb2+ in food was established by aptamer regulating gold nanoparticles reduction. The reduction of HAuCl4 catalyzed by H2O2 is a slow process, and graphene oxide (GO) has excellent catalytic performance for the reaction, which enabled the system to generate gold nanoparticles (AuNPs) with high Raman activity. When the aptamer was introduced into the system, its binding with GO reduced the reaction speed. Upon adding Pb2+ to the system, the aptamer preferentially combined with Pb2+ and GO was released to accelerate the AuNPs production. The concentration of the AuNPs was proportional to the intensity of the added Raman signal molecule 4-MBA and the main Raman peak of Pb2+ appeared at 1595.80 cm−1. The ability of a novel aptamer (M4-16) and traditional aptamers (T30695, TBA) for Pb2+ determination was compared, and the concentration of the aptamer, HAuCl4 and heating time were optimized to build optimal detection system. After several pretreatment of the original SERS spectroscopy, combined with the comparison of various models, the first-order derivative preprocessing combined with competitive adaptive reweighted sampling model achieved the best performance (Rc = 0.9966, Rp = 0.9972), the detection limit for Pb2+ was 0.1 μg L−1. The combination of SERS technology and chemometrics is a promising method that could be used to achieve rapid and highly sensitive detection of Pb2+ in food.

Patulin (PAT) and alternariol (AOH) are the main mycotoxin contaminants in fruits and their products, which have great toxic effects on human body due to their teratogenicity and carcinogenicity. This study proposed a surface enhanced Raman spectroscopy (SERS) technology combining chemometrics and coffee-ring effect to build high-throughput label-free detection models for PAT and AOH. A stable coffee ring structure was built by optimizing the drying temperature and droplet volume. Comparing the partial least squares (PLS) models grounded on variables selection method, the best performance was obtained by using synergy interval (Si) and genetic algorithm (GA) for PAT (Rc = 0.9905, Rp = 0.9759) and AOH (Rc = 0.9829, Rp = 0.9808), respectively. The limits of detection (LOD) for PAT and AOH were as low as 1 μg L−1, and the recovery rates were 92.80%–114.83% with relative standard deviation (RSD) ≤ 4.86 for PAT and 82.06%–108.13% with RSD ≤2.28% for AOH. The SERS technology combined with chemometrics and coffee-ring effect holds promise for high-throughput label-free detection of PAT and AOH in fruits and their products.

Arsenic (As) is one of the toxic, persistent, and lethal heavy metalloids and requires rapid, less costly, and sensitive detection methods. This study proposed a label-free cuprous oxide/silver (Cu2O/Ag) surface-enhanced Raman scattering (SERS) nanoprobe to detect total As in tea. Different total As spiked tea concentrations were mixed with the Cu2O/Ag SERS nanoprobe for the SERS detection. Quantitative models were established for predicting the total As in tea by comparatively applying chemometric algorithms. Amongst the algorithms, competitive adaptive reweighted sampling partial least squares (CARS-PLS) optimized the most effective spectral variables to predict the total As in tea efficiently. The CARS-PLS gave the highest correlation coefficient value (Rp = 0.9935), very low root means square error (RMSEP = 0.0496 μg g−1) in the prediction set and recorded the highest RPD value of 8.819. The proposed nanoprobe achieved a lower detection limit (0.00561 μg g−1), excellent selectivity, satisfactory reproducibility, and stability. No significant difference was recorded when the performance of the Cu2O/Ag total As SERS sensor was compared with the inductively coupled plasma mass spectrometry (ICP-MS) method. Therefore, this developed Cu2O/Ag coupled chemometrics SERS sensing method could be used to efficiently determine, quantify, and predict total As in tea to promote monitoring of heavy metal contaminants.

Highlights • A method that can separate homochromatic FM in mixed congee was proposed. • Spectral features of FM and mixed congee were extracted to build recognition model. • The SVM model achieved high identification rates (99.17%) for homochromatic FM. • 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.

Matcha tea is rich in taste and bioactive constituents, quality evaluation of matcha tea is important to ensure flavor and efficacy. Near-infrared spectroscopy (NIR) in combination with variable selection algorithms was proposed as a fast and non-destructive method for the quality evaluation of matcha tea. Total polyphenols (TP), free amino acids (FAA), and polyphenols-to-amino acids ratio (TP/FAA) were assessed as the taste quality indicators. Successive projections algorithm (SPA), genetic algorithm (GA), and simulated annealing (SA) were subsequently developed from the synergy interval partial least squares (SiPLS). The overall results revealed that SiPLS-SPA and SiPLS-SA models combined with NIR exhibited higher predictive capabilities for the effective determination of TP, FAA and TP/FAA with correlation coefficient in the prediction set (Rp) of Rp > 0.97, Rp > 0.98 and Rp > 0.98 respectively. Therefore, this simple and efficient technique could be practically exploited for tea quality control assessment.

Pseudomonas spp. are the dominant spoilage bacteria which can cause chicken spoilage. Some traditional detection methods are often unsuitable for their rapid real-time detection. Thus, in this paper, a fusion strategy based on colorimetric sensors and near-infrared spectroscopy was applied to rapidly identify Pseudomonas spp. in chicken. First, four different species of Pseudomonas—Pseudomonas gessardii, Pseudomonas psychrophila, Pseudomonas fragi, and Pseudomonas fluorescens—were isolated from putrid chicken, and then, the odor and spectral information of the Pseudomonas species and their mixture were obtained by colorimetric sensors and near-infrared spectroscopy, respectively. Thirty-six odor characteristic variables and 33 spectral characteristic variables were extracted from each technique and used for data fusion based on principal component analysis (PCA). Back-propagation artificial neural network (BP-ANN) was used to build identification model for the discrimination of the different Pseudomonas species. The results showed that the discrimination capability of the model based on data fusion was superior to that based on the two techniques independently, and eventually BP-ANN achieved 100% classification rate by cross-validation and 98.75% classification rate in predication set. This work indicates that the combination of colorimetric sensors and near-infrared spectroscopy is promising for the rapid identification of Pseudomonas species in chicken extract, and hence may be applied towards quality monitoring.

Rare earth-doped upconversion nanoparticles (UCNPs) have a promising potential in biodetection due to their unique frequency upconverting capability and high detection sensitivity. This study developed a novel and sensitive aptamer-based UCNPs (NaY 0.48 Gd 0.3 F 4 :Yb 0.2 , Ho 0.02 ) biosensor for sensing tetracycline (TET) in foodstuffs. The aptamer-magnetic nanoparticles (aptamer-MNPs) and the complementary DNA-UCNPs (cDNA-UCNPs) conjugates were prepared and used as the capture and signal probes, respectively. They formed the complex of MNPs-aptamer-cDNA-UCNPs with complete binding. With TET addition, the aptamer preferentially bound with TET and caused the dissociation of some cDNA; and the liberation of some cDNA-UCNPs. This led to a decreased fluorescent signal on the surface of MNPs. Under the optimal conditions, a wide linear detection range from 0.01 to 100 ng/mL was achieved, with a limit of detection of 0.0062 ng/mL, for detection of TET. The proposed method was successfully applied to measure TET in contaminated samples. Results showed that the proposed UCNPs biosensor offers an efficient, specific, and simple approach for the detection of TET in food, and has high potential for food safety and quality control.

The automated detection of defective apples with a machine vision system is difficult because of the non-uniform intensity distribution on the apple images and the visual similarity between the stem-ends/calyx and the defects. This paper presents a novel method to recognise defective apples by using a machine vision system that combines near-infrared(NIR) coded spot-array structured light and fast lightness correction. By analysing the imaging principle of the spots projected onto the surface of a spherical object, we regard the change in the position of the spots as a coded primitive. A binary-encoded M-array is designed by using primitives as the pattern of the NIR structured light. The stem-ends/calyxes can be identified by analysing a difference matrix from the NIR apple image captured with a multispectral camera. Fast lightness correction is performed to convert the uneven lightness distribution on the apple surface into a uniform lightness distribution over the whole fruit surface. The candidate defective regions segmented and extracted from the RGB apple image captured with the same multispectral camera are classified as the true defects or the stem-ends/calyxes by using the result of the stem-end/calyx identification in the NIR image. The apples are finally classified into sound and defective classes according to the existence or absence of defects respectively. The online experimental result with an average overall recognition accuracy of 90.2% for three apple varieties indicates that the proposed method is effective and suitable for defective apple detection.

Fungal infection is one of the main causes of apple corruption. The main dominant spoilage fungi in causing apple spoilage are storage mainly include Penicillium Paecilomyces paecilomyces (P. paecilomyces), penicillium chrysanthemum (P. chrysogenum), expanded Penicillium expansum (P. expansum), Aspergillus niger (Asp. niger) and Alternaria. In this study, surface-enhanced Raman spectroscopy (SERS) based on gold nanorod (AuNRs) substrate method was developed to collect and examine the Raman fingerprints of dominant apple spoilage fungus spores. Standard normal variable (SNV) was used to pretreat the obtained spectra to improve signal-to-noise ratio. Principal component analysis (PCA) was applied to extract useful spectral information. Linear discriminant analysis (LDA) and non-linear pattern recognition methods including K nearest neighbor (KNN), Support vector machine (SVM) and back propagation artificial neural networks (BPANN) were used to identify fungal species. As the comparison of modeling results shown, the BPANN model established based on the characteristic spectra variables have achieved the satisfactory result with discrimination accuracy of 98.23%; while the PCA-LDA model built using principal component variables achieved the best distinguish result with discrimination accuracy of 98.31%. It was concluded that SERS has the potential to be an inexpensive, rapid and effective method to detect and identify fungal species.

Shortwave near infrared (SWNIR) and long wave near infrared (LWNIR) spectroscopy with a novel color compensation method were compared to predict soluble solids content of apple. Linear and nonlinear regression models were considered. Eventually, independent component analysis-support vector machine (ICA-SVM) models proved to be superior to other nonlinear models. Rp was 0.9398 and RMSEP was 0.3870% for the optimal model of SWNIR, while Rp was 0.9455 and RMSEP was 0.3691% for that of LWNIR. Moreover, the results showed that color compensation could significantly improve the prediction performance of SWNIR model. Our work implies that SWNIR with color compensation has an obvious prospect in practical industrial use for real-time monitoring apple quality.

Near-infrared (NIR) spectroscopy as an emerging analytical technique was used for the first time to quantitatively detect the watercore degree and soluble solids content (SSC) in apple. To reduce the data processing time and meet the needs of practical application, the variable selection methods including synergy interval (SI), successive projections algorithm (SPA), genetic algorithm (GA) and competitive adaptive reweighted sampling (CARS) were used to identify the characteristic variables and simplify the models. The spectral variables closely related to the apple bioactive components were used for the establishment of the partial least squares (PLS) models. The predictive correlation coefficient (Rp), root mean square error of prediction (RMSEP), and residual predictive deviation (RPD) were used to estimate the performance of the models. The CARS-PLS models displayed the best prediction performance using 600–1000 nm spectra with Rp, RMSEP, and RPD values of 0.9562, 1.340% and 3.720 for apple watercore degree; 0.9808, 0.327 oBx and 4.845 for apple SSC, respectively. These results demonstrate the potential of the NIR transmittance spectroscopy technology for quantitative detection of SSC and watercore degree in apple fruit.

A simple, rapid and low-cost analytical method was employed for simultaneous determination of bioactive constituents and antioxidant capability of green tea. The strategy was based on swarm intelligence algorithms with partial least squares (PLS) such as simulated annealing PLS (SA-PLS), ant colony optimization PLS (ACO-PLS), genetic algorithm PLS (GA-PLS), and synergy interval PLS (Si-PLS) coupled with Near-infrared (NIR) spectroscopy. These algorithms were independently applied to select informative spectral variables and improve the prediction of green tea components. Results showed that NIR combined with SA-PLS and Si-PLS had a strong correlation coefficient with the wet-chemical methods for predicting epigallocatechin gallate (Rp2 = 0.97); epigallocatechin (Rp2 = 0.97); epicatechin gallate (Rp2 = 0.96); epicatechin (Rp2 = 0.91); catechin (Rp2 = 0.98); caffeine (Rp2 = 0.96); theanine (Rp2 = 0.93); and antioxidant capability (Rp2 = 0.80) in green tea. Our results revealed the potential utilization of NIR spectroscopy coupled with SA-PLS and Si-PLS algorithms as an effective and robust technique to simultaneously predict active constituents and antioxidant capability of green tea.

Hyperspectral microscope imaging (HMI) technology coupled with chemometrics was attempted to mimic human panel test for estimating sensory quality of matcha in this study. The hypercubes from HMI system contained spatial and spectral information related with quality of samples. Models were established based on the spectral information and the sensory scores from human panel evaluation for sensory attributes. Characteristic spectra were first averaged and extracted from all the pixels in the optimized regions of interest. Then, key spectral variables were selected by competitive adaptive reweighted sampling and used for building artificial neural networks models (namely CARS-ANN models). Results demonstrated that the key spectral variables were only accounted for between 2% and 7% of the original spectral variables for each sensory attribute. The CARS-ANN models achieved superior performance, with 11 spectral variables and 0.7946 of Rp2 (coefficient of determination in prediction set) for appearance, 8 spectral variables and 0.7172 of Rp2 for infusion color, 21 spectral variables and 0.6747 of Rp2 for aroma, 6 spectral variables and 0.7788 of Rp2 for taste, 7 spectral variables and 0.7774 of Rp2 for overall quality. Overall, HMI technology as a rapid, objective and accurate tool has the potential for estimating quality of matcha.

Hyperspectral microscope imaging (HMI) technology as a novel approach was proposed to evaluate physical characteristics of matcha. Particle size distribution as one of the significant physical characteristics was investigated. Data fusion which integrated of textural features from images at 524 nm and key spectral features selected by competitive adaptive reweighed sampling (CARS) were as the raw data for modeling. Models were optimized by cross-validation. Results showed that the performance of models was improved with data fusion. The best ANN models with data fusion were achieved with Rp (correlation coefficient in prediction set) of 0.8020 for D10, 0.8414 for D20, 0.8238 for D30, 0.8124 for D40, 0.8058 for D50, 0.8157 for D60, 0.7643 for D70, 0.7360 for D80, and 0.6313 for D90, respectively. This work demonstrated that HMI technology as a rapid, accurate and high effective protocol has great potential in predicting particle size distribution in matcha powder.

The study assembles a highly sensitive upconversion fluorescence sensor for screening Escherichia coli (E. coli) in food. The principle of the strategy was based on the specific matching between two aptamers embedded magnetic nanoparticles (MNPs) and cDNA-upconversion nanoparticles (UCNPs). The fluorescence emission intensity of the MNPs-aptamer-cDNA-UCNPs conjugate system gradually decreased upon rise in the E coli levels. This change was regarded as a concentration equivalent for E coli at 662 nm. The morphology, crystallinity, vibrational and electronic characterization for the formed MNPs-aptamer-cDNA-UCNPs conjugate were safely assigned. Under the optimum conditions, the fluorescence sensor achieved a lower limit of detection (10 cfu mL−1) in the linear range of 58–58 × 106 cfu mL−1. Furthermore, the applicability of the proposed sensor to detect E coli was examined in adulterated pork samples. The strategy was also validated by a standard plate count method with satisfactory recovery (p > 0.05).

The potential of cyclic voltammetry electronic tongue (CVET) coupled to chemometrics was investigated for measuring total free amino acids (FAA) content in black tea. Two working electrodes - glassy carbon electrode and platinum electrode were comparative and syncretic in prediction of total FAA content. Synergy interval partial least square (Si-PLS) combined with variable combination population analysis (VCPA), namely Si-VCPA-PLS, was creatively employed for searching characteristic variables selection from the whole cyclic voltammetry data set. Cyclic voltammetry signals coupled Si-VCPA-PLS model results represented its feasibility in estimating total FAA content in black tea, achieved with Rp = 0.8185 for the glassy carbon electrode; Rp = 0.8366 for the platinum electrode; and Rp = 0.8414 for their data fusion. The extracted characteristic variables were only occupying about 0.2% of the origin. This work confirmed that the cyclic voltammetry electronic tongue could be as a fast, low-cost, efficient and complementary approach for predicting total FAA content in black tea.

A noise-free bacterial colony counting method identifying noise (i.e., sausage, bacon, and millet fragments) with similar colors or shapes to those of colonies was developed for food quality assessment. First, spectral features corresponding to colony cluster regions and background regions (agar medium and food fragments) were extracted after collection of hyperspectral images. A cluster-segmenting calibration model that could identify colony clusters and background regions was developed. Second, spectral features of colony centers and borders were extracted, and a colony-separating calibration model that could separate single colonies from clusters (multiple colonies contacting each other) was developed. Third, each pixel of an agar plate hyperspectral image was identified using established calibration models, enabling the colonies on the agar plate to be counted successfully (R2 = 0.9998). The results demonstrated that the proposed method could identify the noises caused by food fragments with similar colors or shapes to those of colonies.

The objectives of this research were to compare the effect of different fruit orientations on the quality of acquired spectra and to provide a suitable calibration model for further online determination of soluble solids content (SSC) of “Fuji” apples using visible and near-infrared (Vis/NIR) diffuse transmittance. The diffuse transmittance spectra between 650 and 910 nm were collected with the designed spectrum measurement system in two fruit orientations: stem-calyx axis horizontal (T1) and stem-calyx axis vertical (T2). Area change rate (ACR) was used to evaluate the stability of spectra collected in two fruit orientations. Results showed that the fruit orientation T1 was better for our designed spectrum measurement system. Then, the performance of partial least squares (PLS) models based on spectral data after the pretreatment of several preprocessing methods was analyzed and compared. Finally, the modified competitive adaptive reweighted sampling (MCARS), successive projection algorithm (SPA), and their combination were investigated to select the effective variables for the determination of SSC. It concluded that the MCARS-SPA-PLS model based on the spectra after preprocessing of Savitzky-Golay (SG) smoothing achieved better results for SSC prediction. The correlation coefficients between measured and predicted SSC were 0.962 and 0.946, and the root mean square errors were 0.510 and 0.527°Brix for calibration and prediction set, respectively. Moreover, the physicochemical properties of 27 variables selected by MCARS-SPA were discussed to obtain a better interpretation of the calibration model. The overall results indicated that the designed diffuse transmittance spectrum measurement system together with the PLS calibration model with 27 effective variables selected by MCARS-SPA method had a potential application for online SSC detection of apple.

Hyperspectral imaging technique was investigated to determine the soluble solids content (SSC) and firmness of pears. A total of 160 pear samples were prepared for the calibration (n = 120) and prediction (n = 40) sets. A hyperspectral imaging system was used to acquire hyperspectral reflectance image from each pear in visible and near infrared (400–1000 nm) regions. Mean spectra were extracted from the regions of interest for the hyperspectral image of each pear. Spectral data were first pretreated with different preprocessing techniques and analyzed using partial least square (PLS) to establish calibration models. However, the large size of spectral data contains a large number of redundant variables that lead to complexity and poor predicting ability of calibration models. Several variable selection methods were investigated to select effective wavelength variables for the determination of SSC and firmness of pear. In this study, the variables selected by successive projection algorithm (SPA), competitive adaptive reweighted sampling (CARS) and the combination of CARS and SPA were used for PLS regression. The CARS-SPA-PLS models based on 25 and 22 variables achieved the optimal performance for two internal quality indices compared with full-spectrum PLS, CARS-PLS, and SPA-PLS models. The correlation coefficient (r pre) and root mean square error of prediction (RMSEP) by CARS-SPA-PLS were 0.876, 0.491 for SSC and 0.867, 0.721 for firmness, respectively. The overall results indicated that the CARS-SPA was a powerful way for the selection of effective variables and the hyperspectral imaging system together with CARS-SPA-PLS model could be applied as a fast and potential method for the determination of SSC and firmness of pear.

Informative variable selection or wavelength selection plays an important role in the quantitative analysis of near-infrared (NIR) spectra because the modern spectroscopy instrumentations usually have a high resolution and the obtained spectral data sets may have thousands of variables and hundreds or thousands of samples. In this study, a new combination of Monte Carlo–uninformative variable elimination (MC-UVE) and successive projections algorithm (SPA; MC-UVE-SPA) was proposed to select the most effective variables. MC-UVE was firstly used to eliminate the uninformative variables in the raw spectra data. Then, SPA was applied to determine the variables with the least collinearity. A case study was done based on the NIR spectroscopy for the non-destructive determination of soluble solids content (SSC) in ‘Ya’ pear. A total of 160 samples were prepared for the calibration (n = 120) and prediction (n = 40) sets. Three calibration algorithms including linear regressions of partial least square regression (PLS) and multiple linear regression (MLR), and nonlinear regression of least-square support vector machine (LS-SVM) were used for model establishment by using the selected variables by SPA, UVE, MC-UVE, UVE-SPA, and MC-UVE-SPA, respectively. The results indicated that linear models such as PLS and MLR were more effective than nonlinear model such as LS-SVM in the prediction of SSC of ‘Ya’ pear. In terms of linear models, different variable selection methods can obtain a similar result with the RMSEP values range from 0.2437 to 0.2830. However, combination of MC-UVE and SPA was helpful for obtaining a more parsimonious and efficient model for predicting the SSC values in ‘Ya’ pear. Twenty-two effective variables selected by MC-UVE-SPA achieved the optimal linear MC-UVE-SPA-MLR model compared with other all developed models by balancing between model accuracy and model complexity. The coefficients of determination (r 2), root mean square error of prediction, and residual predictive deviation by MC-UVE-SPA-MLR were 0.9271, 0.2522, and 3.7037, respectively.

Taste sensor technique with multivariate calibration was attempted to determine the contents of catechins (EGCG, EGC and ECG) and caffeine in green tea in this work. The system of data acquisitions based on taste sensor was developed in the experiment. Two multivariate calibrations, which were partial least square (PLS) and artificial neural network with principal component analysis (PCA-ANN), were applied to build forecasting models, respectively. Some parameters were optimized by cross-validation in building model. The performance of the final model was evaluated according to root mean square error of prediction ( RMSEP ) and correlation coefficient ( R ) in the prediction set. Experimental results showed that the PCA-ANN model is superior to the PLS model, and the results of each optimal model were obtained by PCA-ANN as follows: RMSEP (%) = 0.2399, R = 0.9037 for Caffeine model; RMSEP (%) = 0.3101, R = 0.8204 for ECG model; RMSEP (%) = 0.4113, R = 0.8384 for EGC model; RMSEP (%) = 0.6065, R = 0.9473 for EGCG model. This work demonstrated that taste sensor technique with multivariate calibration can be successfully employed to determine main catechins and caffeine contents in green tea.

This paper reported the results of simultaneous analysis of main catechins (i.e., EGC, EC, EGCG and ECG) contents in green tea by the Fourier transform near infrared reflectance (FT-NIR) spectroscopy and the multivariate calibration. Partial least squares (PLS) algorithm was conducted on the calibration of regression model. The number of PLS factors and the spectral preprocessing methods were optimised simultaneously by cross-validation in the model calibration. The performance of the final model was evaluated according to root mean square error of cross-validation (RMSECV), root mean square error of prediction (RMSEP) and correlation coefficient ( R ). The correlations coefficients ( R ) in the prediction set were achieved as follows: R = 0.9852 for EGC model, R = 0.9596 for EC model, R = 0.9760 for EGCG model and R = 0.9763 for ECG model. This work demonstrated that NIR spectroscopy with PLS algorithm could be used to analyse main catechins contents in green tea.