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The widespread occurrence of microplastics (MPs) in the food chain has gained substantial recognition as a pressing concern, highlighting the inevitability of human exposure through ingestion of foodborne MPs, coupled with the release of MPs from plastic packaging. However, there are notable disparities in the reported numbers of MPs in foods and beverages, warranting a thorough investigation into the factors contributing to these discrepancies. Table salt is one of the major sources of MPs, and there was an approximately hundred‐fold difference between the reviewed studies that reported the highest and lowest number of MPs. In addition, more noticeable discrepancies were discovered between studies on MPs released from teabags. One study reported that approximately 15 billion MPs were released into a cup of tea from a single teabag, whereas another research paper found only approximately 106.3 ± 14.6 MP/teabag after brewing. This comprehensive review focuses on the inconsistencies observed across studies examining MPs, shedding light on the plausible factors underlying these variations. Furthermore, the review outlines areas in analytical procedures that require enhancement and offers recommendations to promote accuracy and standardization in future research efforts, such as employing analytical methods capable of confirming the presence of MPs, using appropriate filter sizes, considering representative sample sizes when extrapolation is involved, and so on. By pinpointing the detection processes leading to the inconsistent results observed in MP studies, this comparative analysis will contribute to the development of reliable analytic methods for understanding the extent of microplastic contamination in the human food chain. [ABSTRACT FROM AUTHOR]

Rapid detection of foodborne pathogens, spoilage microbes, and other biological contaminants in complex food matrices is essential to maintain food quality and ensure consumer safety. Traditional methods involve culturing microbes using a range of nonselective and selective enrichment methods, followed by biochemical confirmation among others. The time‐to‐detection is a key limitation when testing foods, particularly those with short shelf lives, such as fresh meat, fish, dairy products, and vegetables. Some recent detection methods developed include the use of spectroscopic techniques, such as matrix‐assisted laser desorption ionization‐time of flight along with hyperspectral imaging protocols.This review presents a comprehensive overview comparing insights into the principles, characteristics, and applications of newer and emerging techniques methods applied to the detection and identification of microbes in food matrices, to more traditional benchtop approaches. The content has been developed to provide specialist scientists a broad view of bacterial identification methods available in terms of their benefits and limitations, which may be useful in the development of future experimental design. The case is also made for incorporating some of these emerging methods into the mainstream, for example, underutilized potential of spectroscopic techniques and hyperspectral imaging. [ABSTRACT FROM AUTHOR]

It is challenging to achieve consistent quantification of hydrogen‐bonded water species by fitting Gaussian bands on spectra collected from different instruments, especially when the spectral resolution is low. Therefore, this work aims to propose a near‐infrared (NIR) multivariate calibration model transfer method to obtain reliable, consistent, and reproducible results from different spectroscopic instruments. A composite method of Gaussian deconvolution, multivariate curve resolution‐alternating least squares (MCR‐ALS), and partial least squares (PLS) was developed to quantify five water species (labeled as C1, C2, C3, C4, and C5), respectively, assigned to non‐hydrogen‐bonded, singly hydrogen‐bonded, doubly and triply hydrogen‐bonded species, distorted ice‐like structures in the liquid phase, and fully hydrogen‐bonded species showing ice‐like structure. Primary instrument (iS50) was selected due to the highest spectral resolution and spectra (7500–5845 cm−1 with spectral resolution of 0.5 cm−1) were acquired from four types of salt solutions. The developed PLS model yielded a high prediction performance when applied on two independent prediction sets with R2P higher than 0.95 and RMSEP less than 0.009 for prediction of C1‐5. Model built with primary spectra was successfully transferred to spectra of secondary instrument (iN10), producing RMSEP less than 0.010. Transferred model applied on secondary instruments (NIRECO and MPA) showed that fully H‐bonded species in deionized water decreased as temperature increased, while non‐hydrogen‐bonded and singly hydrogen‐bonded demonstrated an opposite temperature dependence. Our results confirmed that it is promising to transfer the calibration model from a sophisticated spectrometer to a simple spectroscopic instrument in terms of quantification of hydrogen‐bonded water species. A composite method of Gaussian deconvolution, MCR‐ALS and PLS was proposed to quantify five hydrogen‐bonded water species. The developed model can be successfully transferred from a sophisticated spectrometer to a simple spectroscopic instrument. [ABSTRACT FROM AUTHOR]

Abstract: Array detectors improve data collection speed in hyperspectral chemical imaging, yet are prone to striping noise patterns in the image plane, which is difficult to remove. This type of noise affects spectral features and disturbs visual impression of the scene. We found that this type of noise depends on the material composition and setting parameters, ie, pixel size, and it also varies in accordance with the signal intensity of the observed wavelength. To address this, we proposed a new correction method on the basis of the application of chemometric techniques in the image plane of each wavelength. To verify the effectiveness of this method, infrared transmission images of the 2″ × 2″ positive, 1951 USAF Hi‐Resolution Target and biomaterial samples were obtained with a 16‐element ( 8 × 2) pixel array detector. Point detector images of some samples were also acquired and used as reference images. The proposed correction method produced substantial improvements in the visual impression of intensity images. Principal components analysis was performed to inspect spectral changes after preprocessing, and the results suggested that the major spectral features were not altered while the stripes on intensity images were removed. Spectral profiles and principal components analysis loadings inspection confirmed the smoothing ability of this correction method. As traditional preprocessing techniques, standard normal variate and derivative transformation were not able to remove line artifacts, especially on the biomaterial images. Overall, the proposed method was effective for removing striping noise patterns from infrared images with a minimal alteration of the valuable hyperspectral image information. [ABSTRACT FROM AUTHOR]

Hydrogels are an important class of biomaterials that can absorb large quantities of water. In this study, changes in hydration of natural hydrogels (agar, chitosan, gelatin, starch, and blends of each with chitosan) during storage and rehydration were studied by using near-infrared hyperspectral imaging (NIR-HSI).Moisture content was calculated based on changes in sample weight during hydration. The NIR-HSI data were acquired by using a push-broom system operating in diffuse reflectance in the wavelength range 943 to 1650 nm. A novel synthesis method was developed to enable common preparation of each hydrogel. Mean spectra obtained from the hyperspectral images were analyzed, and predictive models for moisture content were developed by using partial least squares regression. Models were compared in predictive performance by using an independent validation set of data. The optimal model in predictive performance was a 1 latent variable partial least squares regression model developed on second derivative and mean centered pseudoabsorbance data in the wavelength range 943 to 1272 nm. This model was applied to pixel spectra from samples in the validation set to inspect spatial variations during dehydration and rehydration. Challenges associated with NIR-HSI of hydrogels with a large variation in moisture content are discussed. [ABSTRACT FROM AUTHOR]

Mealiness is known as an important internal quality attribute of fruits/vegetables, which has significant influence on consumer purchasing decisions. Mealiness has been a topic of research interest over the past several decades. A number of destructive and nondestructive techniques are introduced for mealiness detection. Nondestructive methods are more interesting because they are rapid, noninvasive, and suitable for real-time purposes. In this review, the concept of mealiness is presented for potato, apple, and peach, followed by an in-depth discussion about applications of destructive and nondestructive techniques developed for mealiness detection. The results suggest the potential of electromagnetic-based techniques for nondestructive mealiness evaluation. Further investigations are in progress to find more appropriate nondestructive techniques as well as cost and performance. [ABSTRACT FROM AUTHOR]

This work proposes a new method to treat spatial and spectral information interactively. The method extracts spatial features, ie, variogram, gray‐level co‐occurrence matrix (GLCM), histograms of oriented gradients (HOG), and local binary pattern (LBP) features, from each wavelength image of hypercube and principal component analysis (PCA) is applied on this spatial feature matrix to identify wavelength‐dependent variation in spatial patterns. Resultant image is obtained by projecting the score values to the original data. Three datasets, including a synthetic hyperspectral image (Dataset 1), a set of real hyperspectral images of salmon fillets (Dataset 2), and remote‐sensing images (Dataset 3), were utilized to evaluate the performance of the proposed method. Results from Dataset 1 showed that the spatial‐spectral methods had the potential of reducing baseline offset noise. Dataset 2 revealed that spatial‐spectral methods can alleviate noisy pixels with strong signal and reduce shadow effects. In addition, substantial improvements were obtained in case of classification between white stripe and red muscle pixels by using the HOG‐based approach with correct classification rate (CCR) of 0.97 compared with the models directly built from raw and standard normal variate (SNV) preprocessed spectra (CCR = 0.94). Samson image of Dataset 3 suggested the flexibility and effectiveness of the proposed method by improving CCR of 0.96 using conventional PCA on SNV pretreated spectra to 0.98 using GLCM‐based approach on SNV preprocessed spectra. Overall, experimental results demonstrated that the spatial‐spectral methods can improve the results found by using the spectral information alone because of the spatial information provided. This work proposes a new method to treat spatial and spectral information interactively. The proposed method was tested on three datasets, including synthetic and real‐world hyperspectral images. Results demonstrated that the introduced spatial‐spectral method (a) has the potential to reduce baseline offset noise, (b) can alleviate noisy pixels with strong signal, and (c) can improve the classification performance. In summary, the proposed spatial‐spectral method enables to extract meaningful and useful information from hyperspectral images. [ABSTRACT FROM AUTHOR]