Detection of Sulfite Dioxide Residue on the Surface of Fresh-Cut Potato Slices Using Near-Infrared Hyperspectral Imaging System and Portable Near-Infrared Spectrometer

Discrimination of untreated and sodium sulphite treated bean sprouts by Fourier transform infrared spectroscopy and chemometrics

Sprouts of black beans (Phaseolus vulgaris L.), soybeans (Glycine max L.) and mung beans (Vigna radiata L.) are widely consumed foods containing abundant nutrients with biological activities. They are commonly treated with sulphites for the preservation and extension of shelf-life. However, our previous investigation found that immersing the bean sprouts in sulphite might convert the active components into sulphur-containing derivatives, which can affect both the quality and safety of the sprouts. This study explores the use of FTIR in conjunction with chemometric techniques to differentiate between non-immersed (NI) and sodium sulphite immersed (SI) black bean, soybean and mung bean sprouts. A total of 168 batches of raw spectra were obtained from NI and SI-bean sprouts using FTIR spectroscopy. Four pre-processing techniques, three modelling assessment techniques and four model evaluation indices were examined for differences in performance. The results show that the multiplicative scatter correction is the most effective pre-processing method. Among the models, the accuracy rate of the three models was as follows: radial basis function neural network (95%) > convolutional neural network (91%) > random forest (82%). The overall findings indicate that FTIR spectroscopy, in conjunction with appropriate chemometric approaches, has a high potential for rapidly determining the difference between NI and SI-bean sprouts.

Portable NIR spectroscopy: the route to green analytical chemistry

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

A Review of Recent Advances for the Detection of Biological, Chemical, and Physical Hazards in Foodstuffs Using Spectral Imaging Techniques

Traditional methods for detecting foodstuff hazards are time-consuming, inefficient, and destructive. Spectral imaging techniques have been proven to overcome these disadvantages in detecting foodstuff hazards. Compared with traditional methods, spectral imaging could also increase the throughput and frequency of detection. This study reviewed the techniques used to detect biological, chemical, and physical hazards in foodstuffs including ultraviolet, visible and near-infrared (UV-Vis-NIR) spectroscopy, terahertz (THz) spectroscopy, hyperspectral imaging, and Raman spectroscopy. The advantages and disadvantages of these techniques were discussed and compared. The latest studies regarding machine learning algorithms for detecting foodstuff hazards were also summarized. It can be found that spectral imaging techniques are useful in the detection of foodstuff hazards. Thus, this review provides updated information regarding the spectral imaging techniques that can be used by food industries and as a foundation for further studies.

Non-Destructive Screening of Sodium Metabisulfite Residue on Shrimp by SERS with Copy Paper Loaded with AgNP

In order to prompt the appearance of the shrimp color, sodium metabisulfite is frequently added in shrimp processing, which is, however, prohibited in China and many other countries. This study aimed to establish a surface-enhanced Raman spectroscopy (SERS) method for screening sodium metabisulfite residues on shrimp surfaces, in a non-destructive manner. The analysis was carried out using a portable Raman spectrometer jointly with copy paper loaded with silver nanoparticles as the substrate material. The SERS response of sodium metabisulfite gives two fingerprint peaks at 620 (strong) and 927 (medium) cm^-1, respectively. This enabled unambiguous confirmation of the targeted chemical. The sensitivity of the SERS detection method was determined to be 0.1 mg/mL, which was equal to residual sodium metabisulfite on the shrimp surface at 0.31 mg/kg. The quantitative relationship between the 620 cm^-1 peak intensities and the concentrations of sodium metabisulfite was established. The linear fitting equation was y = 2375x + 8714 with R² = 0.985. Reaching an ideal balance in simplicity, sensitivity, and selectivity, this study demonstrates that the proposed method is ideally suitable for in-site and non-destructive screening of sodium metabisulfite residues in seafood.

Novel insight into the role of sulfur dioxide in fruits and vegetables: Chemical interactions, biological activity, metabolism, applications, and safety

Sulfur dioxide (SO2) are a category of chemical compounds widely used as additives in food industry. So far, the use of SO2 in fruit and vegetable industry has been indispensable although its safety concerns have been controversial. This article comprehensively reviews the chemical interactions of SO2 with the components of fruit and vegetable products, elaborates its mechanism of antimicrobial, anti-browning, and antioxidation, discusses its roles in regulation of sulfur metabolism, reactive oxygen species (ROS)/redox, resistance induction, and quality maintenance in fruits and vegetables, summarizes the application technology of SO2 and its safety in human (absorption, metabolism, toxicity, regulation), and emphasizes the intrinsic metabolism of SO2 and its consequences for the postharvest physiology and safety of fresh fruits and vegetables. In order to fully understand the benefits and risks of SO2, more research is needed to evaluate the molecular mechanisms of SO2 metabolism in the cells and tissues of fruits and vegetables, and to uncover the interaction mechanisms between SO2 and the components of fruits and vegetables as well as the efficacy and safety of bound SO2. This review has important guiding significance for adjusting an applicable definition of maximum residue limit of SO2 in food.

Hyperspectral Characteristic Band Selection and Estimation Content of Soil Petroleum Hydrocarbon Based on GARF-PLSR

With continuous improvements in oil production, the environmental problems caused by oil exploitation are becoming increasingly serious. Rapid and accurate estimation of soil petroleum hydrocarbon content is of great significance to the investigation and restoration of environments in oil-producing areas. In this study, the content of petroleum hydrocarbon and the hyperspectral data of soil samples collected from an oil-producing area were measured. For the hyperspectral data, spectral transforms, including continuum removal (CR), first- and second-order differential (CR-FD, CR-SD), and Napierian logarithm (CR-LN), were applied to eliminate background noise. At present, there are some shortcomings in the method of feature band selection, such as large quantity, time of calculation, and unclear importance of each feature band obtained. Meanwhile, redundant bands easily exist in the feature set, which seriously affects the accuracy of the inversion algorithm. In order to solve the above problems, a new method (GARF) for hyperspectral characteristic band selection was proposed. It combined the advantage that the grouping search algorithm can effectively reduce the calculation time with the advantage that the point-by-point search algorithm can determine the importance of each band, which provided a clearer direction for further spectroscopic research. The 17 selected bands were used as the input data of partial least squares regression (PLSR) and K-nearest neighbor (KNN) algorithms to estimate soil petroleum hydrocarbon content, and the leave-one-out method was used for cross-validation. The root mean squared error (RMSE) and coefficient of determination (R²) of the estimation result were 3.52 and 0.90, which implemented a high accuracy with only 8.37% of the entire bands. The results showed that compared with the traditional characteristic band selection methods, GARF can effectively reduce the redundant bands and screen out the optimal characteristic bands in the hyperspectral data of soil petroleum hydrocarbon with the method of importance assessment, which retained the physical meaning. It provided a new idea for the research of other substances in soil.

Intelligent detection for fresh-cut fruit and vegetable processing: Imaging technology

Fresh-cut fruits and vegetables are healthy and convenient ready-to-eat foods, and the final quality is related to the raw materials and each step of the cutting unit. It is necessary to integrate suitable intelligent detection technologies into the production chain so as to inspect each operation to ensure high product quality. In this paper, several imaging technologies that can be applied online to the processing of fresh-cut products are reviewed, including: multispectral/hyperspectral imaging (M/HSI), fluorescence imaging (FI), X-ray imaging (XRI), ultrasonic imaging, thermal imaging (TI), magnetic resonance imaging (MRI), terahertz imaging, and microwave imaging (MWI). The principles, advantages, and limitations of these imaging technologies are critically summarized. The potential applications of these technologies in online quality control and detection during the fresh-cut processing are comprehensively discussed, including quality of raw materials, contamination of cutting equipment, foreign bodies mixed in the processing, browning and microorganisms of the cutting surface, quality/shelf-life evaluation, and so on. Finally, the challenges and future application prospects of imaging technology in industrialization are presented.

Ferulic Acid Treatment Maintains the Quality of Fresh-Cut Taro (Colocasia esculenta) During Cold Storage

Taro (Colocasia esculenta) is a major root crop or vegetable in the world, and the corm is a good source of many nutrients including starch, vitamins, and minerals. Taro corms are processed into various forms before consumption, which makes them perishable, reduces the shelf life, and increases postharvest losses. The surface browning of fresh-cut taros is one of the major factors that limits storage life and affects consumer acceptance. In this study, the effects of ferulic acid (FA) as an effective agent in the prevention of quality deterioration were investigated. Fresh-cut taros were immersed in distilled water and different concentrations of FA (1, 2, 5, 10, and 20 mM) solutions for 30 min, air-dried at 25°C for 30 min, and then stored at 5°C for 12 days to investigate the effects of FA on browning. Among the FA concentrations tested, 10 mM resulted in significantly higher L * values, lower a * and b *, and browning index values. FA treatment (10 mM) also induced de novo biosynthesis of two volatile compounds, including non-anal and octanoic acid ethyl ester in fresh-cut taros following extended cold storage. The results suggest that FA treatment maintains the quality of fresh-cut taros under cold conditions. FA treatment enhanced PAL activity and gene expression but reduced total phenolic content and the expression of six C4H, 4CL, and CHS genes, suggesting that FA treatment reduced phenolic biosynthesis. FA treatment reduced PPO activity and gene expression and decreased soluble quinone content, suggesting that FA treatment suppressed the phenolic oxidation. FA treatment enhanced the activity and gene expression of CAT and POD, reduced those of LOX, and decreased MDA and H2O2 levels, suggesting that FA treatment activated the antioxidant defense system and thereby reduced oxidative damage. These findings demonstrated that FA treatment could serve as an effective approach to retard the browning of fresh-cut taros and provided a basis for the feasible application of FA in the preservation of fresh-cut foods.

Imaging Spectroscopy and Machine Learning for Intelligent Determination of Potato and Sweet Potato Quality

Imaging spectroscopy has emerged as a reliable analytical method for effectively characterizing and quantifying quality attributes of agricultural products. By providing spectral information relevant to food quality properties, imaging spectroscopy has been demonstrated to be a potential method for rapid and non-destructive classification, authentication, and prediction of quality parameters of various categories of tubers, including potato and sweet potato. The imaging technique has demonstrated great capacities for gaining rapid information about tuber physical properties (such as texture, water binding capacity, and specific gravity), chemical components (such as protein, starch, and total anthocyanin), varietal authentication, and defect aspects. This paper emphasizes how recent developments in spectral imaging with machine learning have enhanced overall capabilities to evaluate tubers. The machine learning algorithms coupled with feature variable identification approaches have obtained acceptable results. This review briefly introduces imaging spectroscopy and machine learning, then provides examples and discussions of these techniques in tuber quality determinations, and presents the challenges and future prospects of the technology. This review will be of great significance to the study of tubers using spectral imaging technology.

Development of a novel noninvasive quantitative method to monitor Siraitia grosvenorii cell growth and browning degree using an integrated computer-aided vision technology and machine learning

The rapid, accurate and noninvasive detection of biomass and plant cell browning can provide timely feedback on cell growth in plant cell culture. In this study, Siraitia grosvenorii suspension cells were taken as an example, a phenotype analysis platform was successfully developed to predict the biomass and the degree of cell browning based on the color changes of cells in computer-aided vision technology. First, a self-made laboratory system was established to obtain images. Then, matrices were prepared from digital images by a self-developed high-throughput image processing tool. Finally, classification models were used to judge different cell types, and then a semi-supervised classification to predict different degrees of cell browning. Meanwhile, regression models were developed to predict the plant cell mass. All models were verified with a good agreement by biological experiments. Therefore, this method can be applied for low-cost biomass estimation and browning degree quantification in plant cell culture.

A study of starch content detection and the visualization of fresh-cut potato based on hyperspectral imaging

Fresh-cut potatoes are popular with consumers because of their healthiness, hygiene, and convenience. Currently, starch content is mainly detected using chemical methods, which are time-consuming and laborious. Moreover, these methods may cause some side effects in the human body. Therefore, suitable methods are required for the rapid and accurate detection of starch content. In this study, Zihuabai and Atlantic potatoes were used as experimental samples. The potatoes were sliced with stainless-steel blades, and images of these potatoes were obtained through hyperspectral imaging. The images were preprocessed using different methods. Competitive adaptive reweighed sampling (CARS) and the successive projection algorithm (SPA) were used to extract characteristic wavelengths. A partial least squares regression (PLSR) model was constructed to predict the starch content from the preprocessed full spectrum and the spectrum under the characteristic wavelength. The results indicate that the full spectrum model constructed through standard normal variable transformation (SNV) preprocessing had the best performance, with a correlation coefficient in the calibration set (R c) value of 0.9020, a root mean square error of correction (RMSEC) of 2.06, and a residual prediction deviation (RPD) of 2.33. The characteristic wavelength-based multivariate scattering correction (MSC)-CARS-PLSR model exhibited better performance than the PLSR model constructed using the full spectrum, with an R c value of 0.9276, RMSEC of 1.76, correlation coefficient in the prediction set (R p) value of 0.9467, root mean square error of prediction of 1.63, and RPD of 2.95. The starch content in fresh-cut potatoes was visualized using the best model in combination with pseudocolor technology. The results indicate that hyperspectral imaging is effective for mapping the spatial distribution of starch content; thus, a solid theoretical basis is obtained for the grading and online monitoring of fresh-cut potato slices.

Hyperspectral Imaging Tera Hertz System for Soil Analysis: Initial Results

Analyzing soils using conventional methods is often time consuming and costly due to their complexity. These methods require soil sampling (e.g., by augering), pretreatment of samples (e.g., sieving, extraction), and wet chemical analysis in the laboratory. Researchers are seeking alternative sensor-based methods that can provide immediate results with little or no excavation and pretreatment of samples. Currently, visible and infrared spectroscopy, electrical resistivity, gamma ray spectroscopy, and X-ray spectroscopy have been investigated extensively for their potential utility in soil sensing. Little research has been conducted on the application of THz (Tera Hertz) spectroscopy in soil science. The Tera Hertz band covers the frequency range between 100 GHz and 10 THz of the electromagnetic spectrum. One important feature of THz radiation is its correspondence with the particle size of the fine fraction of soil minerals (clay < 2 µm to sand < 2 mm). The particle size distribution is a fundamental soil property that governs soil water and nutrient content, among other characteristics. The interaction of THz radiation with soil particles creates detectable Mie scattering, which is the elastic scattering of electromagnetic waves by particles whose diameter corresponds approximately to the wavelength of the radiation. However, single-spot Mie scattering spectra are difficult to analyze and the understanding of interaction between THz radiation and soil material requires basic research. To improve the interpretation of THz spectra, a hyperspectral imaging system was developed. The addition of the spatial dimension to THz spectra helps to detect relevant features. Additionally, multiple samples can be scanned in parallel and measured under identical conditions, and the high number of data points within an image can improve the statistical accuracy. Technical details of the newly designed hyperspectral imaging THz system working from 250 to 370 GHz are provided. Results from measurements of different soil samples and buried objects in soil demonstrated its performance. The system achieved an optical resolution of about 2 mm. The sensitivity of signal damping to the changes in particle size of 100 µm is about 10 dB. Therefore, particle size variations in the µm range should be detectable. In conclusion, automated hyperspectral imaging reduced experimental effort and time consumption, and provided reliable results because of the measurement of hundreds of sample positions in one run. At this stage, the proposed setup cannot replace the current standard laboratory methods, but the present study represents the initial step to develop a new automated method for soil analysis and imaging.