Total lipid prediction in single intact cocoa beans by hyperspectral chemical imaging

Research on variety identification of common bean seeds based on hyperspectral and deep learning

Accurate, fast and non-destructive identification of varieties of common bean seeds is important for the cultivation and efficient utilization of common beans. This study is based on hyperspectral and deep learning to identify the varieties of common bean seeds non-destructively. In this study, the average spectrum of 3078 hyperspectral images from 500 varieties was obtained after image segmentation and sensitive region extraction, and the Synthetic Minority Over-sampling Technique (SMOTE) was used to achieve the equilibrium of the samples of various varieties. A one-dimensional convolutional neural network model (IResCNN) incorporating Inception module and residual structure was proposed to identify seed varieties, and Support Vector Machine (SVM), K-Nearest Neighbor (KNN), VGG19, AlexNet, ResNet50 were established to compare the identification effect. After analyzing the effects of multiple spectral preprocessing methods on the model, the study selected Savitzky-Golay smoothing correction (SG) for spectral preprocessing and extracted 66 characteristic wavelengths using Successive Projections Algorithm (SPA) as inputs to the discriminative model. Ultimately, the IResCNN model achieved the highest accuracy of 93.06 % on the test set, indicating that hyperspectral technology can accurately identify bean varieties, and the study provides a correct method of thinking for the non-destructive classification of multi-species small-sample bean varieties.

Chromatographic and spectroscopic methods for the detection of cocoa butter in cocoa and its derivatives: A review

Currently, there is fierce competition in the cocoa industry to develop products that possess distinctive sensory characteristics and flavours. This is because cocoa and its derivatives provide numerous health and functional advantages, which is essential to their economics. The fatty acid and triglyceride composition of cocoa determines its quality. This review emphasises the necessity of developing precise, adaptable analytical techniques to identify and quantify cocoa butter in cocoa and its derived products, from cocoa beans to chocolate bars. Key chromatographic and spectroscopic techniques play crucial roles in understanding the fundamental principles underlying the production of cocoa with desirable flavours. This significantly impacts the sustainability, traceability, and authenticity of cocoa products while also supporting the battle against adulteration.

Proximate Content Monitoring of Black Soldier Fly Larval (Hermetia illucens) Dry Matter for Feed Material using Short-Wave Infrared Hyperspectral Imaging

Edible insects are gaining popularity as a potential future food source because of their high protein content and efficient use of space. Black soldier fly larvae (BSFL) are noteworthy because they can be used as feed for various animals including reptiles, dogs, fish, chickens, and pigs. However, if the edible insect industry is to advance, we should use automation to reduce labor and increase production. Consequently, there is a growing demand for sensing technologies that can automate the evaluation of insect quality. This study used short-wave infrared (SWIR) hyperspectral imaging to predict the proximate composition of dried BSFL, including moisture, crude protein, crude fat, crude fiber, and crude ash content. The larvae were dried at various temperatures and times, and images were captured using an SWIR camera. A partial least-squares regression (PLSR) model was developed to predict the proximate content. The SWIR-based hyperspectral camera accurately predicted the proximate composition of BSFL from the best preprocessing model; moisture, crude protein, crude fat, crude fiber, and crude ash content were predicted with high accuracy, with R2 values of 0.89 or more, and root mean square error of prediction values were within 2%. Among preprocessing methods, mean normalization and max normalization methods were effective in proximate prediction models. Therefore, SWIR-based hyperspectral cameras can be used to create automated quality management systems for BSFL.

Non-destructive determination of ginsenosides in ginseng by combined hyperspectral and X-ray techniques based on ensemble learning

The potential of hyperspectral imaging and X-ray techniques for the non-destructive determination of the ginsenosides Rg1 + Re and Rb1 in ginseng was investigated. The random forest (RF) models were established using spectral information extracted from hyperspectral data to predict ginsenosides content. The RF model was optimized by data pre-processing methods and feature screening methods. Multiple feature screening methods combined with partial least squares regression models were used to find hyperspectral image feature information (color information and texture information) related to ginsenosides. A significant positive correlation between density extracted from X-ray images and the ginsenosides content was found by building the univariate linear regression models. Finally, the prediction performance of the integrated learning model based on the three data blocks was better than the model constructed by single data blocks (Rg1 + Re: R2p = 0.8691, RMSEP = 0.0439%; Rb1: R2p = 0.8291, RMSEP = 0.0803%). The results indicate that the developed method is highly feasible for non-destructive evaluation of ginseng quality.

Rapid and Nondestructive Identification of Origin and Index Component Contents of Tiegun Yam Based on Hyperspectral Imaging and Chemometric Method

Tiegun yam is a typical food and medicine agricultural product, which has the effects of nourishing the kidney and benefitting the lungs. The quality and price of Tiegun yam are affected by its origin, and counterfeiting and adulteration are common. Therefore, it is necessary to establish a method to identify the origin and index component contents of Tiegun yam. Hyperspectral imaging combined with chemometrics was used, for the first time, to explore and implement the identification of origin and index component contents of Tiegun yam. The origin identification models were established by partial least squares-discriminant analysis (PLS-DA), support vector machine (SVM), and random forest (RF) using full wavelength and feature wavelength. Compared with other models, MSC-PLS-DA is the best model, and the accuracy of the training set and prediction set is 100% and 98.40%. Partial least squares regression (PLSR), random forest (RF), and support vector regression (SVR) models were used to predict the contents of starch, polysaccharide, and protein in Tiegun yam powder. The optimal residual predictive deviation (RPD) values of starch, polysaccharide, and protein prediction models selected in this study were 5.21, 3.21, and 2.94, respectively. The characteristic wavelength extracted by the successive projections algorithm (SPA) method can achieve similar results as the full-wavelength model. These results confirmed the application of hyperspectral imaging (HSI) in the identification of the origin and the rapid nondestructive prediction of starch, polysaccharide, and protein contents of Tiegun yam powder. Therefore, the HSI combined with the chemometric method was available for conveniently and accurately determining the origin and index component contents of Tiegun yam, which can expect to be an attractive alternative method for identifying the origin of other food.

Impact of superheated steam roasting on changes in antioxidant and microstructure properties of raw and processed cocoa cotyledon

This research focused on the roasting of cocoa beans at 184 °C for 16 min duration in a superheated steam oven using two separate modes of heating: convection mode and superheated steam mode. After roasting, the antioxidant properties of the cooked cocoa were assessed as ferric reducing antioxidant power activity (FRAP), DPPH radical scavenging activity, total flavonoid content (TFC) and total phenol content (TPC). The micro structural properties of raw and processed cocoa beans were observed using scanning electron microscopy (SEM). As discovered in the scan, conventional roasting showed a nearly complete rapture of the cytoplasmic network system and the destruction of the organelles, whereas superheated steam mode showed satisfactory images. Studies indicated that superheated steam roasting preserved significantly (p < 0.05) greater antioxidant properties as opposed to conventional method of roasting.

Intelligent detection of hard seeds of snap bean based on hyperspectral imaging

As a common problem in snap beans, hard seed has seriously affected the large-scale industrial planting and yield of snap bean. To realize accurate, quick and non-destructive identifying the hard seeds of snap bean is of great significance to avoiding the effects of hard seeds on germination and growth. This research was based on hyperspectral imaging (HSI) to achieve accurate detection of hard seeds of snap bean. This study obtained the characteristic spectra from the hyperspectral image of a single seed, and then combined the synthetic minority over-sampling technique (SMOTE) and Tomek links to balance the numbers of hard and non-hard seed samples. The characteristic wavelengths were extracted from the average spectrum. Then the average spectrum was processed by first derivative (1D). After that, the characteristic wavelengths could be extracted using successive projections algorithm (SPA). Finally, a radial basis function-support vector machine (RBF-SVM) model was established to realize the intelligent detection of hard seeds, and the detection accuracy rate reached 89.32%. The research results showed that HSI technology could achieved accurate, fast and non-destructive testing of the hard seeds of snap bean, which is of great significance to the large-scale and standardized planting of snap bean and increase the yield per unit area.

Application of Computer Microtomography and Hyperspectral Imaging to Assess the Homogeneity of the Distribution of Active Ingredients in Functional Food

Functional foods represent one of the most intensively investigated and widely promoted areas in the food and nutrition sciences’ market today. The purpose of this work is to determine the possibility of using computed microtomography to assess the homogeneity of distribution of active pharmaceutical ingredients (vitamins K and D and calcium) throughout chocolate. Algorithms for analyzing of microtomographic images were proposed to quantify the distribution of active pharmaceutical ingredients (API) in chocolate: the Gray Level Co-Occurrence Matrix, quadtree decomposition and hyperspectral imaging. The use of the methods of analysis and processing of microtomographic images allows for a quantitative assessment of the homogeneity of the distribution of components throughout the sample, without a 3D reconstruction process. In computer microtomography analysis, it is possible to assess the distribution of those components whose density differs by at least a unit in the accepted scale of gray levels of images and for grain sizes not smaller than the voxel size. The proposed image analysis algorithms, Gray Level Co-Occurrence Matrix, quadtree decomposition and hyperspectral imaging, allow for the assessment of distribution of active ingredients in chocolate.

Polysaccharide prediction in Ganoderma lucidum fruiting body by hyperspectral imaging

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Predicting the concentration of polysaccharides by hyperspectral images of the Ganoderma lucidum cap is feasible.

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Establishing calibration models using visible and near-infrared spectroscopy respectively to find out the characteristic spectrum.

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Exploring the influence of different tissue parts on prediction through ROI selection.

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Prediction of polysaccharide concentration in the full life cycle of the Ganoderma lucidum fruiting body.

Ganoderma lucidum is a traditional Chinese healthy food with many kinds of nutritious activities, and polysaccharide is one of its main active components. Ganoderma lucidum polysaccharide plays a vital role in improving human immunity and anti-oxidation. At present, the methods of detecting polysaccharide content of Ganoderma lucidum are destructive, and the steps are complicated and time-consuming. This study aims to explore the possibility of using hyperspectral imaging (HSI) to predict polysaccharide content in a nondestructive way during the growth of Ganoderma lucidum. The partial least square regression (PLSR) model shows good performance for Ganoderma lucidum (Rp2 = 0.924, RPDp = 3.622) with pretreatment method of Savitzky-Golay (SG) and standard normal variate (SNV), and feature selection method of successive projections algorithm (SPA). This study indicates that HSI can quickly and nondestructive detect the polysaccharide content of Ganoderma lucidum, provide guidance for the cultivation industry and improve the economic benefits of Ganoderma lucidum.

Rapid Detection of Nonprotein Nitrogen Adulterants in Milk Powder Using Point-Scan Raman Hyperspectral Imaging Technology

To develop a rapid detection method for nonprotein nitrogen adulterants, this experiment sets up a set of point-scan Raman hyperspectral imaging systems to qualitatively distinguish and quantitatively and positionally analyze samples spiked with a single nonprotein nitrogen adulterant and samples spiked with a mixture of nine nonprotein nitrogen adulterants at different concentrations (5 × 10-3 to 2.000%, w/w). The results showed that for samples spiked with single nonprotein nitrogen adulterants, the number of pixels corresponding to the adulterant in the region of interest increased linearly with an increase in the analyte concentration, the average coefficient of determination (R 2) was above 0.99, the minimum detection concentration of nonprotein nitrogen adulterants reached 0.010%, and the relative standard deviation (RSD) of the predicted concentration was less than 6%. For the sample spiked with a mixture of nine nonprotein nitrogen adulterants, the standard curve could be used to accurately predict the additive concentration when the additive concentration was greater than 1.200%. The detection method established in this study has good accuracy, high sensitivity, and strong stability. It provides a method for technical implementation of real-time and rapid detection of adulterants in milk powder at the port site and has good application and promotion prospects.

Coffee berry and green bean chemistry - Opportunities for improving cup quality and crop circularity

Coffee cup quality is primarily determined by the type and variety of green beans chosen and the roasting regime used. Furthermore, green coffee beans are not only the starting point for the production of all coffee beverages but also are a major source of revenue for many sub-tropical countries. Green bean quality is directly related to its biochemical composition which is influenced by genetic and environmental factors. Post-harvest, on-farm processing methods are now particularly recognised as being influential to bean chemistry and final cup quality. However, research on green coffee has been limited and results are fragmented. Despite this, there are already indications that multiple factors play a role in determining green coffee chemistry - including plant cultivation/fruit ripening issues and ending with farmer practices and post-harvest storage conditions. Here, we provide the first overview of the knowledge determined so far specifically for pre-factory, green coffee composition. In addition, the potential of coffee waste biomass in a biobased economy context for the delivery of useful bioactives is described as this is becoming a topic of growing relevance within the coffee industry. We draw attention to a general lack of consistency in experimentation and reporting and call for a more intensive and united effort to build up our knowledge both of green bean composition and also how perturbations in genetic and environmental factors impact bean chemistry, crop sustainability and ultimately, cup quality.

Rapid detection of chlorpyrifos residue in rice using surface-enhanced Raman scattering coupled with chemometric algorithm

Due to the continuous development and progress of society and more and more attention to the quality and safety of food, rapid testing of pesticides in food is of great significance. In this paper, surface-enhanced Raman spectroscopy (SERS) and chemometric algorithms were employed collectively to quantify chlorpyrifos (CP) residues in rice samples. The SERS spectra from different concentrations (0.01-1000 μg/mL) of CP were collected using AgNPs-deposited-ZnO nanoflower (NFs)-like nanoparticles (Ag@ZnO NFs) SERS sensor. Four quantitative chemometric models for CP were comparatively studied, and the competitive adaptive reweighted sampling-partial least squares model achieved the best prediction and practical applicability for predicting CP levels with a limit of detection of 0.01 µg/mL. The results of the student's t-test showed no significant difference between this method and high-performance liquid chromatography (HPLC), and good relative standard deviation (RSD) indicated that this method could be used for the detection of CP in rice.

Vis-NIR Hyperspectral Imaging for Online Quality Evaluation during Food Processing: A Case Study of Hot Air Drying of Purple-Speckled Cocoyam (Colocasia esculenta (L.) Schott)

In this study, hyperspectral imaging (HSI) and chemometrics were implemented to develop prediction models for moisture, colour, chemical and structural attributes of purple-speckled cocoyam slices subjected to hot-air drying. Since HSI systems are costly and computationally demanding, the selection of a narrow band of wavelengths can enable the utilisation of simpler multispectral systems. In this study, 19 optimal wavelengths in the spectral range 400–1700 nm were selected using PLS-BETA and PLS-VIP feature selection methods. Prediction models for the studied quality attributes were developed from the 19 wavelengths. Excellent prediction performance (RMSEP < 2.0, r2P > 0.90, RPDP > 3.5) was obtained for MC, RR, VS and aw. Good prediction performance (RMSEP < 8.0, r2P = 0.70–0.90, RPDP > 2.0) was obtained for PC, BI, CIELAB b*, chroma, TFC, TAA and hue angle. Additionally, PPA and WI were also predicted successfully. An assessment of the agreement between predictions from the non-invasive hyperspectral imaging technique and experimental results from the routine laboratory methods established the potential of the HSI technique to replace or be used interchangeably with laboratory measurements. Additionally, a comparison of full-spectrum model results and the reduced models demonstrated the potential replacement of HSI with simpler imaging systems.