Rapid analysis of wheat flour by different handheld near-infrared spectrometers: A discussion of calibration model maintenance and performance comparison

Benchtop and different miniaturized near-infrared spectrometers application study: Calibration transfer and 2D-COS for in-situ analysis of moisture content in HPMC

The demand for miniaturized near-infrared (NIR) spectrometers has surged due to their potential for in-situ analysis. However, their predictive accuracy has not yet matched that of traditional benchtop instruments. This study evaluates the effectiveness of rapid quantitative moisture analysis in hydroxypropyl methylcellulose (HPMC) using a benchtop spectrometer, Antaris II from Thermo Fisher Scientific Inc., and five miniaturized spectrometers (MicroNIR 1700 from Viavi Solutions, OTO-SW2540 from OtOPhotonics, IAS DLP 1700 from Dallas, NIRONE Sensor 2.2 from Helsinki, and NIRS M1800 from Alian Optoelectronics). This study employed an Improved Principal Component Analysis (IPCA) transfer method to standardize spectra from the diverse miniaturized NIR spectrometers, facilitating calibration transfer across different spectroscopic technologies. The benchtop (Antaris II) delivered the most superior results, indicating that miniaturized spectrometers must refine their methodologies to approach the predictive performance of benchtop counterparts. Further, this work conducted a two-dimensional correlation spectroscopy (2D-COS) analysis on the spectra from various spectrometers. This analysis bolstered the partial least squares regression (PLSR) model, highlighting discrepancies between miniaturized and benchtop spectrometers and deepening understanding of the factors influencing the PLSR models. The IPCA leverages the benchtop model to enhance the precision and reliability of miniaturized NIR spectrometers. This innovative and versatile research approach aims to further optimize the performance of miniaturized NIR spectrometers for specific applications, representing a significant step forward in their development.

Hyperspectral imaging for detection of macronutrients retained in glutinous rice under different drying conditions

This study detected the macronutrients retained in glutinous rice (GR) under different drying conditions by innovatively applying visible-near infrared hyperspectral imaging coupled with different spectra preprocessing and effective wavelength selection techniques (EWs). Subsequently, predictive models were developed based on processed spectra for the detection of the macronutrients, which include protein content (PC), moisture content (MC), fat content (FC), and ash content (AC). The result shows the raw spectra-based model had a prediction accuracy (R p 2 ) of 0.6493, 0.9521, 0.4594, and 0.9773 for PC, MC, FC, and AC, respectively. Applying Savitzky Golay first derivatives (SG1D) method increases theR p 2 value to 0.9972, 0.9970, 0.9857 and 0.9972 for PC, MC, FC, and AC, respectively. Using the variable iterative space shrinkage algorithm (VISSA) as EWs reduces the spectral bands by over 60%, and this increases the accuracy of the model (SG1D-VISSA-PLSR) to 100%. Therefore, the developed SGID-VISSA-PLSR can be used to build a smart and reliable spectral system for detecting the macronutrients in GR grains.

A decision support tool to analyze the properties of wheat, cocoa beans and mangoes from their NIR spectra

Near infrared spectroscopy (NIRS) is an analytical technique that offers a real advantage over laboratory analysis in the food industry due to its low operating costs, rapid analysis, and non-destructive sampling technique. Numerous studies have shown the relevance of NIR spectra analysis for assessing certain food properties with the right calibration. This makes it useful in quality control and in the continuous monitoring of food processing. However, the NIR calibration process is difficult and time-consuming. Analysis methods and techniques vary according to the configuration of the NIR instrument, the sample to be analyzed and the attribute that is to be predicted. This makes calibration a challenge for many manufacturers. This paper aims to provide a data-driven methodology for developing a decision support tool based on the smart selection of NIRS wavelength to assess various food properties. The decision support tool based on the methodology has been evaluated on samples of cocoa beans, grains of wheat and mangoes. Promising results were obtained for each of the selected models for the moisture and fat content of cocoa beans (R2cv: 0.90, R2test: 0.93, RMSEP: 0.354%; R2cv: 0.73, R2test: 0.79, RMSEP: 0.913%), acidity and vitamin C content of mangoes (R2cv: 0.93, R2test: 0.97, RMSEP: 17.40%; R2cv: 0.66, R2test: 0.46, RMSEP: 0.848%), and protein content of wheat-DS2 (R2cv: 0.90, R2test:0.92, RMSEP: 0.490%) respectively. Moreover, the proposed approach allows results to be obtained that are better than benchmarks for the moisture and protein content of wheat-DS1 (R2cv: 0.90, R2test: 94, RMSEP: 0.337%; R2cv: 0.99, R2test: 0.99, RMSEP: 0.177%), respectively. PRACTICAL APPLICATION: This research introduces a practical tool aimed at determining the quality of food by identifying specific light wavelengths. However, it is important to acknowledge potential challenges, such as overfitting. Before implementation, it is crucial for further research to address and mitigate the issues to ensure the reliability and accuracy of the solution. If successfully applied, this tool could significantly enhance the accuracy of near-infrared spectroscopy in assessing food quality attributes. This advancement would provide invaluable support for decision-making in industries involved in food production, ultimately leading to better overall product quality for consumers.

Analytical Chemistry Strategies in the Use of Miniaturised NIR Instruments: An Overview

Miniaturized NIR instruments have been increasingly used in the last years, and they have become useful tools for many applications on a broad variety of samples. This review focuses on miniaturized NIR instruments from an analytical point of view, to give an overview of the analytical strategies used in order to help the reader to set up their own analytical methods, from the sampling to the data analysis. It highlights the uses of these instruments, providing a critical discussion including current and future trends.

Predicting wheat gluten concentrations in potato starch using GPR and SVM models built by terahertz time-domain spectroscopy

The purpose of this study was for the first time to explore the feasibility of terahertz (THz) spectral imaging for the detection of gluten contents in food samples. Based on the obtained 80 THz spectrum data, Gaussian process regression (GPR) and support vector machine (SVM) models were established to predict wheat gluten concentrations in 40 potato starch mixture samples. The prediction performances of GPR and SVM obtained were R2 = 0.859 and RMSE = 0.070, and R2 = 0.715 and RMSE = 0.101 in the gluten concentration range of 1.3%-100%, respectively, showing that the linear SVM algorithm had better prediction performance. The results indicated that THz spectral imaging combined with GPR could be used to predict the gluten content in food samples. It is thus hoped that this research should provide a novel technique for gluten content detection to ensure gluten-free food samples.

The Application of Handheld Near-Infrared Spectroscopy and Raman Spectroscopic Imaging for the Identification and Quality Control of Food Products

The following investigations describe the potential of handheld NIR spectroscopy and Raman imaging measurements for the identification and authentication of food products. On the one hand, during the last decade, handheld NIR spectroscopy has made the greatest progress among vibrational spectroscopic methods in terms of miniaturization and price/performance ratio, and on the other hand, the Raman spectroscopic imaging method can achieve the best lateral resolution when examining the heterogeneous composition of samples. The utilization of both methods is further enhanced via the combination with chemometric evaluation methods with respect to the detection, identification, and discrimination of illegal counterfeiting of food products. To demonstrate the solution to practical problems with these two spectroscopic techniques, the results of our recent investigations obtained for various industrial processes and customer-relevant product examples have been discussed in this article. Specifically, the monitoring of food extraction processes (e.g., ethanol extraction of clove and water extraction of wolfberry) and the identification of food quality (e.g., differentiation of cocoa nibs and cocoa beans) via handheld NIR spectroscopy, and the detection and quantification of adulterations in powdered dairy products via Raman imaging were outlined in some detail. Although the present work only demonstrates exemplary product and process examples, the applications provide a balanced overview of materials with different physical properties and manufacturing processes in order to be able to derive modified applications for other products or production processes.

Quantitative assessment of wheat quality using near-infrared spectroscopy: A comprehensive review

Wheat is one of the most widely cultivated crops throughout the world. A great need exists for wheat quality assessment for breeding, processing, and products production purposes. Near-infrared spectroscopy (NIRS) is a rapid, low-cost, simple, and nondestructive assessment method. Many advanced studies associated with NIRS for wheat quality assessment have been published recently, either introducing new chemometrics or attempting new assessment parameters to improve model robustness and accuracy. This review provides a comprehensive overview of NIRS methodology including its principle, spectra pretreatments, spectral wavelength selection, outlier disposal, dataset division, regression methods, and model evaluation. More importantly, the applications of NIRS in the determination of analytical parameters, rheological parameters, and end product quality of wheat are summarized. Although NIRS showed great potential in the quantitative determination of analytical parameters, there are still challenges in model robustness and accuracy in determining rheological parameters and end product quality for wheat products. Future model development needs to incorporate larger databases, integrate different spectroscopic techniques, and introduce cutting-edge chemometrics methods. In addition, calibration based on external factors should be considered to improve the predicted results of the model. The NIRS application in micronutrients needs to be extended. Last, the idea of combining standard product sensory attributes and spectra for model development deserves further study.

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.