Explore the vast range of titles available.

During the past decade, hyperspectral imaging (HSI) has been rapidly developing and widely applied in the food industry by virtue of the use of chemometric techniques in which wavelength selection methods play an important role. This paper is a review of such variable selection methods and their limitations, describing the basic taxonomy of the methods and their respective advantages and disadvantages. Special attention is paid to recent developments in wavelength selection techniques for HSI in the field of food quality and safety evaluations. Typical and commonly used methods in HSI, such as partial least squares regression, stepwise regression and spectrum analysis, are described in detail. Some sophisticated methods, such as successive projections algorithm, uninformative variable elimination, simulated annealing, artificial neural network and genetic algorithm methods, are also discussed. Finally, new methods not currently used but that could have substantial impact on the field are presented. In short, this review provides an overview of wavelength selection methods in food-related areas and offers a thoughtful perspective on future potentials and challenges in the development of HSI systems.

Previous studies have shown the great potential of using plasma-activated water (PAW) on improving agriculture seed germination, however, information on the influence of PAW on crop plantlet juice remains scanty. In this research, the effect of PAW generated by atmosphere pressure Ar–O 2 plasma jet for 1–5 min on wheat seed germination, seedling growth and nutritional properties of wheat plantlet juice was investigated. Results revealed that all PAWs could enhance wheat seed germination and seedling growth in 7 days by improving the germination rate, germination index, fresh weight, dry weight and vigour index, and especially that PAW activated for 3 min (PAW-3) showed the best overall performance. In addition, the application of PAWs enhanced the nutritional properties of wheat plantlet juice from those grown for 14 days by improving total soluble solids, protein content, photosynthetic pigments, total phenolic content, antioxidant activity, enzyme activity, free amino acids and minerals content, and the best enhancement was also observed in PAW-3. It was concluded that PAWs would be an effective technique to enhance the growth and nutritional properties of crop sprouts, which could be served as functional foods in many forms.

Hyperspectral imaging is built with the aggregation of imaging, spectroscopy and radiometric techniques. This technique observes the sample behaviour when it is exposed to light and interprets the properties of the biological samples. As hyperspectral imaging helps in interpreting the sample at the molecular level, it can distinguish very minute changes in the sample composition from its scatter properties. Hyperspectral data collection depends on several parameters such as electromagnetic spectrum wavelength range, imaging mode and imaging system. Spectral data acquired using a hyperspectral imaging system contain variations due to external factors and imaging components. Moreover, food samples are complex matrices with conditions of surface and internal heterogeneities, which may lead to variations in acquired data. Hence, before extracting information, these variations and noises must be reduced from the data using reference-dependent or reference-independent spectral pre-processing techniques. Using of the entire hyperspectral data for information extraction is tedious and time-consuming. In order to overcome this, exploratory data analysis techniques are used to select crucial wavelengths from the excessive hyperspectral data. Using appropriate chemometric techniques (supervised or unsupervised learning techniques) on this pre-processed hyperspectral data, qualitative or quantitative information from sample can be obtained. Qualitative information for analysing of the chemical composition, detecting of the defects and determining the purity of the food product can be extracted using discriminant analysis techniques. Quantitative information including variation in chemical constituents and contamination levels in food and agricultural sample can be extracted using categorical regression techniques. In combination with appropriate spectra pre-processing and chemometric technique, hyperspectral imaging stands out as an advanced quality evaluation system for food and agricultural products.

The freezing medium temperature and the freezing rate are two important parameters that affect the quality of frozen product. The traditional measurement of freezing parameters will destroy the integrity of the sample and can only be implemented during the freezing process. This study aimed to develop nondestructive hyperspectral imaging (HSI) methods to rapidly detect freezing parameters. The spectral features of the porcine meat samples in frozen state were studied, in which 90 pieces of porcine samples were frozen by different methods with different freezing medium (air and liquid) at different temperatures (from −20 to −120 °C) and freezing rates (from 0.307 to 5.1 cm/h). The result showed that the freezing process would strongly influence spectra of the frozen sample. The reflectance increased with the decrease in freezing medium temperatures, and the negative correlation reached a highly significant level. The freezing parameters did not change the position of the spectral peaks but altered the spectral intensity. Most changes were near 1070, 1172, 1420, 1586, and 1890 nm. The partial least-squares regression spectral models exhibited good performance for predicting freezing medium temperatures R c 2 = 0.898 R p 2 = 0.844 and freezing rates R c 2 = 0.879 R p 2 = 0.829 . The study confirmed that could be used for measuring freezing parameters of frozen product. This novel method will not damage the sample integrity, and measurement can be implemented anytime rather than only during the freezing process by traditional methods.

The starches and flours from four different rice cultivars were evaluated for composition, crystallinity characteristics, blue value, turbidity, swelling power, solubility, pasting properties, and textural and retrogradation properties. The amylose content of starches and flours from different rice cultivars differed significantly. The results showed that the physicochemical properties of rice starch and rice flour were correlated to amylose content. The crystallinity degree of rice starch and flour depended on amylose content. The blue value, turbidity value, and gel hardness were positively correlated to amylose content; however, the swelling power, solubility, and gel adhesiveness were negatively correlated to amylose content. Furthermore, the pasting properties and gel textural and retrogradation properties of rice flours were related to the structure properties of rice starch. And the characteristics of starch, protein, and lipid significantly influenced the turbidity, pasting properties, and gel textural and retrogradation properties of rice flours.

The aim of this work was to investigate the effect of chemical oxidation on the emulsifying properties of myofibrillar proteins. Myofibrillar proteins were oxidized by a hydroxyl radical generating system (Fenton reaction). Structural changes of oxidized or non-oxidized myofibrillar proteins were determined using surface hydrophobicity (H 0 ) and Fourier transform infrared (FTIR) spectroscopy. The results suggested that H 0 increased ( p  < 0.05) after treatment with oxidizing agent. Result from FTIR suggested that protein aggregation occurred and there was an increase in β-sheet structure accompanied by a decrease in turns, alpha helix, and random structures with the increase of oxidizing agent. Changes in zeta potential of the test emulsions suggested that protein oxidation could alter the electric charge of myofibrillar proteins. The analysis of the emulsions showed that protein oxidation had a negative effect on the emulsifying properties of myofibrillar proteins due to changes in electric charge, surface active properties, and protein molecular flexibility.

High pressure processing is a food processing method which has shown great potential in the food industry. Similar to heat treatment, high pressure processing inactivates microorganisms, denatures proteins and extends the shelf life of food products. But in the meantime, unlike heat treatments, high pressure treatment can also maintain the quality of fresh foods, with little effects on flavour and nutritional value. Furthermore, the technique is independent of the size, shape or composition of products. In this paper, many aspects associated with applying high pressure as a processing method in the food industry are reviewed, including operating principles, effects on food quality and safety and most recent commercial and research applications. It is hoped that this review will promote more widespread applications of the technology to the food industry.

The potential of visible and near infrared (VIS/NIR) hyperspectral imaging was investigated as a rapid and nondestructive technique to determine whether fish has been frozen–thawed. A total of 108 halibut ( Psetta maxima ) fillets were studied, including 48 fresh and 60 frozen–thawed (F-T) samples. Regarding the F-T samples, two speeds of freezing (fast and slow) were tested. The hyperspectral images of fillets were captured using a pushbroom hyperspectral imaging system in the spectral region of 380 to 1,030 nm. All images were calibrated for reflectance, followed by the minimum noise fraction rotation to reduce the noise. A region-of-interest (ROI) at the image center was selected, and the average spectral data were generated from the ROI image. Dimension reduction was carried out on the ROI image by principal component analysis. The first three principal components (PCs) explained over 98 % of variances of all spectral bands. Gray-level co-occurrence matrix analysis was implemented on the three PC images to extract 36 textural feature variables in total. Least squares-support vector machine classification models were developed to differentiate between fresh and F-T fish based on (1) spectral variables; (2) textural variables; (3) combined spectral and textural variables, respectively. Satisfactory average correct classification rate of 97.22 % for the prediction samples based on (3) was achieved, which was superior to the results based on (1) or (2). The results turned worse when different freezing rates were taken into consideration to classify three groups of fish. The overall results indicate that VIS/NIR hyperspectral imaging technique is promising for the reliable differentiation between fresh and F-T fish.

Microbial contamination during fish flesh spoilage process can easily induce food-borne outbreaks and consumer health problems. Hyperspectral imaging in the spectral range of 400–1000 nm was developed to measure the Escherichia coli ( E. coli ) loads in grass carp fish for evaluation and visualization of microbial spoilage. Partial least square regression (PLSR) model was conducted to build prediction models between the spectral data and the reference E. coli loads estimated by classical microbiological plating method. The PLSR model based on full wavelengths showed good performance on predicting E. coli loads with the residual predictive deviation (RPD) of 5.47 and determination coefficient of R 2 P  = 0.880. Six characteristic wavelengths were selected by the weighted regression coefficients from PLSR analysis and used to simplify the models. The simplified PLSR and multiple linear regression (MLR) models also presented good prediction capability. The better simplified MLR model (RPD = 5.22 and R 2 P  = 0.870) was used to transfer each pixel in the image for visualizing the spatial distribution of E. coli loads. The results demonstrated that hyperspectral imaging technique with multivariate analysis has the potential to rapidly and non-invasively quantify and visualize the E. coli loads in grass carp fish flesh during the spoilage process.

Pollution of environmental endocrine disruptors has caused increasing concern globally. In the current study, a simple colorimetric method with high sensitivity and good selectivity for 17β-estradiol (E2) detection was developed, which employed gold nanoparticles (AuNPs) as colorimetric probe for specific recognition of shortening DNA aptamer. Visible color change from bright red to violet was observed for aggregation of AuNPs without the protection of DNA aptamer. After optimization, the method exhibited great performance for E2 detection with good linearity between E2 concentrations from 0.2 to 5 nM and the absorbance ratio at 620 and 523 nm, with the limit of detection of 0.1 nM. The method was also successfully applied to E2 determination in different spiked water samples including fishpond water, lake water, and tap water, in which good recoveries from 93.1 to 108.9% and acceptable relative standard deviations from 3.4 to 8.9% were obtained. This technique showed great potential for on-site fast determination of E2 in environmental water samples.