Utilizing near infrared hyperspectral imaging for quantitatively predicting adulteration in tapioca starch

A new spectral simulating method based on near-infrared hyperspectral imaging for evaluation of antibiotic mycelia residues in protein feeds

Antibiotic mycelia residues (AMRs) contain antibiotic residues. If AMRs are ingested in excess by livestock, it may cause health problems. To address the current problem of unknown pixel-scale adulteration concentration in NIR-HSI, this paper innovatively proposes a new spectral simulation method for the evaluation of AMRs in protein feeds. Four common protein feeds (soybean meal (SM), distillers dried grains with solubles (DDGS), cottonseed meal (CM), and nucleotide residue (NR)) and oxytetracycline residue (OR) were selected as study materials. The first step of the method is to simulate the spectra of pixels with different adulteration concentrations using a linear mixing model (LMM). Then, a pixel-scale OR quantitative model was developed based on the simulated pixel spectra combined with local PLS based on global PLS scores (LPLS-S) (which solves the problem of nonlinear distribution of the prediction results due to the 0%-100% content of the correction set). Finally, the model was used to quantitatively predict the OR content of each pixel in hyperspectral image. The average value of each pixel was calculated as the OR content of that sample. The implementation of this method can effectively overcome the inability of PLS-DA to achieve qualitative identification of OR in 2%-20% adulterated samples. In compared to the PLS model built by averaging the spectra over the region of interest, this method utilizes the precise information of each pixel, thereby enhancing the accuracy of the detection of adulterated samples. The results demonstrate that the combination of the method of simulated spectroscopy and LPLS-S provides a novel method for the detection and analysis of illegal feed additives by NIR-HSI.

Identification of common buckwheat (Fagopyrum esculentum Moench) adulterated in Tartary buckwheat (Fagopyrum tataricum (L.) Gaertn) flour based on near-infrared spectroscopy and chemometrics

Near-infrared spectroscopy (NIRS) presents great potential in the identification of food adulteration due to its advantages of nondestructive, simple, and easy to operate. In this paper, a method based on NIRS and chemometrics was proposed to predict the content of common buckwheat (Fagopyrum esculentum Moench) flour in Tartary buckwheat (Fagopyrum tataricum (L.) Gaertn) flour. Partial least squares regression (PLSR) and support vector regression (SVR) models were used to analyze the spectrum data of adulterated samples and predict the adulteration level. Various preprocessing methods, parameter-optimization methods, and competitive adaptive reweighted sampling (CARS) wavelength-selection methods were used to optimize the model prediction accuracy. The results of PLSR and SVR modeling for predicting of Tartary buckwheat adulteration content were satisfactory, and the correlation coefficients of the optimum identification models were above 0.99. In conclusion, the combinations of NIRS and chemometrics indicated excellent predictive performance and applicability to analyze the adulteration of common buckwheat flour in Tartary buckwheat flour. This work provides a promising method to identify the adulteration of Tartary buckwheat flour and results obtained can give theoretical and data support for adulteration identification of agro-products.

Rapid Prediction of Adulteration Content in Atractylodis rhizoma Based on Data and Image Features Fusions from Near-Infrared Spectroscopy and Hyperspectral Imaging Techniques

Atractylodis rhizoma (AR) is an herb and food source with great economic, medicinal, and ecological value. Atractylodes chinensis (DC.) Koidz. (AC) and Atractylodes lancea (Thunb.) DC. (AL) are its two botanical sources. The commercial fraud of AR adulterated with Atractylodes japonica Koidz. ex Kitam (AJ) frequently occurs in pursuit of higher profit. To quickly determine the content of adulteration in AC and AL powder, two spectroscopic techniques, near-infrared spectroscopy (NIRS) and hyperspectral imaging (HSI), were introduced. The partial least squares regression (PLSR) algorithm was selected for predictive modeling of AR adulteration levels. Preprocessing and feature variable extraction were used to optimize the prediction model. Then data and image feature fusions were developed to obtain the best predictive model. The results showed that if only single-spectral techniques were considered, NIRS was more suitable for both tasks than HSI techniques. In addition, by comparing the models built after the data fusion of NIRS and HSI with those built by the single spectrum, we found that the mid-level fusion strategy obtained the best models in both tasks. On this basis, combined with the color-texture features, the prediction ability of the model was further optimized. Among them, for the adulteration level prediction task of AC, the best strategy was combining MLF data (at CARS level) and color-texture features (C-TF), at which time the R2T, RMSET, R2P, and RMSEP were 99.85%, 1.25%, 98.61%, and 5.06%, respectively. For AL, the best approach was combining MLF data (at SPA level) and C-TF, with the highest R2T (99.92%) and R2P (99.00%), as well as the lowest RMSET (1.16%) and RMSEP (2.16%). Therefore, combining data and image features from NIRS and HSI is a potential strategy to predict the adulteration content quickly, non-destructively, and accurately.

Assessment of Nitrite Content in Vienna Chicken Sausages Using Near-Infrared Hyperspectral Imaging

Sodium nitrite is a food additive commonly used in sausages, but legally, the unsafe levels of nitrite in sausage should be less than 80 mg/kg, since higher levels can be harmful to consumers. Consumers must rely on processors to conform to these levels. Therefore, the determination of nitrite content in chicken sausages using near infrared hyperspectral imaging (NIR-HSI) was investigated. A total of 140 chicken sausage samples were produced by adding sodium nitrite in various levels. The samples were divided into a calibration set (n = 94) and a prediction set (n = 46). Quantitative analysis, to detect nitrate in the sausages, and qualitative analysis, to classify nitrite levels, were undertaken in order to evaluate whether individual sausages had safe levels or non-safe levels of nitrite. NIR-HSI was preprocessed to obtain the optimum conditions for establishing the models. The results showed that the model from the partial least squares regression (PLSR) gave the most reliable performance, with a coefficient of determination of prediction (R_p) of 0.92 and a root mean square error of prediction (RMSEP) of 15.603 mg/kg. The results of the classification using the partial least square-discriminant analysis (PLS-DA) showed a satisfied accuracy for prediction of 91.30%. It was therefore concluded that they were sufficiently accurate for screening and that NIR-HSI has the potential to be used for the fast, accurate and reliable assessment of nitrite content in chicken sausages.

Non-Destructive Classification of Organic and Conventional Hens' Eggs Using Near-Infrared Hyperspectral Imaging

Eggs that are produced using organic methods retail at higher prices than those produced using conventional methods, but they cannot be differentiated reliably using visual methods. Eggs can therefore be fraudulently mislabeled in order to increase their wholesale and retail prices. The objective of this research was therefore to test near-infrared hyperspectral imaging (NIR-HSI) to identify whether an egg has been produced using organic or conventional methods. A total of 210 organic and 210 conventional fresh eggs were individually scanned using NIR-HSI to obtain absorbance spectra for discrimination analysis. The physical properties of each egg were also measured non-destructively in order to analyze the performance of discrimination compared with those of the NIR-HSI spectral data. Principal component analysis (PCA) showed variation for PC1 and PC2 of 57% and 23% and 94% and 4% based on physical properties and the spectral data, respectively. The best results of the classification using NIR-HSI spectral data obtained an accuracy of 96.03% and an error rate of 3.97% via partial least squares-discriminant analysis (PLS-DA), indicating the possibility that NIR-HSI could be successfully used to rapidly, reliably, and non-destructively differentiate between eggs that had been produced using organic methods from eggs that had been produced using conventional methods.

Quantifying Microalgae Growth by the Optical Detection of Glucose in the NIR Waveband

Microalgae have become a popular area of research over the past few decades due to their enormous benefits to various sectors, such as pharmaceuticals, biofuels, and food and feed. Nevertheless, the benefits of microalgae cannot be fully exploited without the optimization of their upstream production. The growth of microalgae is commonly measured based on the optical density of the sample. However, the presence of debris in the culture and the optical absorption of the intercellular components affect the accuracy of this measurement. As a solution, this paper introduces the direct optical detection of glucose molecules at 940-960 nm to accurately measure the growth of microalgae. In addition, this paper also discusses the effects of the presence of glucose on the absorption of free water molecules in the culture. The potential of the optical detection of glucose as a complement to the commonly used optical density measurement at 680 nm is discussed in this paper. Lastly, a few recommendations for future works are presented to further verify the credibility of glucose detection for the accurate determination of microalgae's growth.

Advances in Machine Learning and Hyperspectral Imaging in the Food Supply Chain

Food quality and safety are the essential hot issues of social concern. In recent years, there has been a growing demand for real-time food information, and non-destructive testing is gradually replacing traditional manual sensory testing and chemical analysis methods with lagging and destructive effects and has strong potential for application in the food supply chain. With the maturity and development of computer science and spectroscopic techniques, machine learning and hyperspectral imaging (HSI) have been widely demonstrated as efficient detection techniques that can be applied to rapidly evaluate sensory characteristics and quality attributes of food products nondestructively and efficiently. This paper first briefly described the basic concepts of hyperspectral imaging and machine learning, including the imaging process of HSI, the type of algorithms contained in machine learning, and the data processing flow. Secondly, this paper provided an objective and comprehensive overview of the current applications of machine learning and HSI in the food supply chain for sorting, packaging, transportation, storage, and sales, based on the state-of-art literature from 2017 to 2022. Finally, the potential of the technology is further discussed to provide optimized ideas for practical application.

Evaluation of Mutton Adulteration under the Effect of Mutton Flavour Essence Using Hyperspectral Imaging Combined with Machine Learning and Sparrow Search Algorithm

The evaluation of mutton adulteration faces new challenges because of mutton flavour essence, which achieves a similar flavour between the adulterant and mutton. Hence, methods for classifying and quantifying the adulterated mutton under the effect of mutton flavour essence, based on near-infrared hyperspectral imaging (NIR-HSI, 1000–2500 nm) combined with machine learning (ML) and sparrow search algorithm (SSA), were proposed in this study. After spectral preprocessing via first derivative combined with multiple scattering correction (1D + MSC), classification and quantification models were established using back propagation neural network (BP), extreme learning machine (ELM) and support vector machine/regression (SVM/SVR). SSA was further used to explore the global optimal parameters of these models. Results showed that the performance of models improves after optimisation via the SSA. SSA-SVM achieved the optimal discrimination result, with an accuracy of 99.79% in the prediction set; SSA-SVR achieved the optimal prediction result, with an R_P² of 0.9304 and an RMSEP of 0.0458 g·g⁻¹. Hence, NIR-HSI combined with ML and SSA is feasible for classification and quantification of mutton adulteration under the effect of mutton flavour essence. This study can provide a theoretical and practical reference for the evaluation and supervision of food quality under complex conditions.

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.

Application of near-infrared spectroscopy for the nondestructive analysis of wheat flour: A review

The quality and safety of wheat flour are of public concern since they are related to the quality of flour products and human health. Therefore, efficient and convenient analytical techniques are needed for the quality and safety controls of wheat flour. Near-infrared (NIR) spectroscopy has become an ideal technique for assessing the quality and safety of wheat flour, as it is a rapid, efficient and nondestructive method. The application of NIR spectroscopy in the quality and safety analysis of wheat flour is addressed in this review. First, we briefly summarize the basic knowledge of NIR spectroscopy and chemometrics. Then, recent advances in the application of NIR spectroscopy for chemical composition, technological parameters, and safety analysis are presented. Finally, the potential of NIR spectroscopy is discussed. Combined with chemometric methods, NIR spectroscopy has been used to detect chemical composition, technological parameters, deoxynivalenol, adulterants and additives of wheat flour. Furthermore, NIR spectroscopy has shown great potential for the rapid and online analysis of the quality and safety of wheat flour. It is anticipated that the current review will serve as a reference for the future analysis of wheat flour by NIR spectroscopy to ensure the quality and safety of flour products.

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NIR spectroscopy is an ideal technique for analysis of wheat flour due to its rapid and nondestructive nature.

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Use of NIR spectroscopy for chemical composition, technological parameters, and safety analysis.

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Online and handheld NIR spectrometers for wheat flour detection are the future trends.

Arrowroot and Cassava Mixed Starch Products Identification by Raman Analysis with Chemometrics

Food frauds present a major problem in the foodstuff industry. Arrowroot and cassava may be targeted in adulteration and falsification processes. Raman analysis combined with chemometric techniques was proposed to identify the mixing and adulteration of these foodstuffs in commercial products. 67 cassava and 5 arrowroot samples were prepared in laboratory. 21 cassava and 5 arrowroot commercial samples were purchased in local stores. Raman assays were performed in the range of 400 to 2300 cm−1. Principal component analysis with K-means clustering was used to identify the adulteration of these products. It was possible to observe the separation of three different groups in the data, these groups labelled group 1, 2 and 3 were correspondent to cassava-like samples, mixed samples, and arrowroot-like samples, respectively. Despite the visual analysis related to sensory characteristics and the visual analysis of each Raman spectrum of cassava and arrowroot not being able to differentiate these foodstuffs, the chemometric approaches with the Raman specters data were able to identify which samples were pure arrowroot, pure cassava and which were mixed products. The proposed approach showed to be an effective tool in the investigation of fraud for arrowroot and cassava.

Determination of hardness for maize kernels based on hyperspectral imaging

In order to realize rapid and non-destructive detection of hardness for maize kernels, a method for quantitative hardness measurement was proposed based on hyperspectral imaging technology. Firstly, the regression model of hardness and moisture content was established. Then, based on reflectance hyperspectral imaging at wavelengths within 399.75-1005.80 nm, the prediction model of the moisture content was studied by the partial least squares regression (PLSR) based on the characteristic wavelengths, which was selected through successive projection algorithm (SPA). Finally, the hardness prediction model was validated by combing the prediction model of moisture content with the regression model of hardness. The coefficient of determination (R²), the root mean square error (RMSE) the ratio of performance-to-deviation (RPD) and the ratio of error range (RER) of hardness prediction were 0.912, 17.76 MPa, 3.41 and 14, respectively. Therefore, this study provided a method for rapid and non-destructive detection of hardness of maize kernels.