Assessing the influence of temperature on NIRS prediction models for the determination of sodium content in dry-cured ham slices

A robust deep learning model for predicting green tea moisture content during fixation using near-infrared spectroscopy: Integration of multi-scale feature fusion and attention mechanisms

Fixation is a critical step in green tea processing, and the moisture content of the leaves after fixation is a key indicator of the fixation quality. Near-infrared spectroscopy (NIRS)-based moisture detection technology is often applied in the tea processing industry. However, temperature fluctuations during processing can cause changes in the NIRS curves, which in turn affect the accuracy of moisture prediction models based on the spectral data. To address this challenge, NIRS data were collected from samples at various stages of fixation and at different temperatures, and a novel deep learning network (DiSENet) was proposed, which integrates multi-scale feature fusion and attention mechanisms. Using a global modeling approach, the proposed method achieved a coefficient of determination (RP2) of 0.781 for moisture content prediction, with a root mean square error (RMSEP) of 1.720 % and a residual predictive deviation (RPD) of 2.148. On the dataset constructed for this study, DiSENet demonstrated superior predictive accuracy compared to the spectral correction methods of external parameter orthogonalization (EPO) and generalized least squares weighting (GLSW), as well as traditional global modeling methods such as partial least squares regression (PLSR) and support vector regression (SVR). This approach effectively corrects spectral interferences caused by temperature variations, thereby enhancing the accuracy of moisture content prediction. Thus, it offers a reliable solution for real-time, non-destructive moisture detection during tea processing.

The Role of Near-Infrared Spectroscopy in Food Quality Assurance: A Review of the Past Two Decades

During food quality control, NIR technology enables the rapid and non-destructive determination of the typical quality characteristics of food categories, their origin, and the detection of potential counterfeits. Over the past 20 years, the NIR results for a variety of food groups-including meat and meat products, milk and milk products, baked goods, pasta, honey, vegetables, fruits, and luxury items like coffee, tea, and chocolate-have been compiled. This review aims to give a broad overview of the NIRS processes that have been used thus far to assist researchers employing non-destructive techniques in comparing their findings with earlier data and determining new research directions.

Microbiological and chemical hazards in cultured meat and methods for their detection

Cultured meat, which involves growing meat in a laboratory rather than breeding animals, offers potential benefits in terms of sustainability, health, and animal welfare compared to conventional meat production. However, the cultured meat production process involves several stages, each with potential hazards requiring careful monitoring and control. Microbial contamination risks exist in the initial cell collection from source animals and the surrounding environment. During cell proliferation, hazards may include chemical residues from media components such as antibiotics and growth factors, as well as microbial issues from improper bioreactor sterilization. In the differentiation stage where cells become muscle tissue, potential hazards include residues from scaffolding materials, microcarriers, and media components. Final maturation and harvesting stages risk environmental contamination from nonsterile conditions, equipment, or worker handling if proper aseptic conditions are not maintained. This review examines the key microbiological and chemical hazards that must be monitored and controlled during the manufacturing process for cultured meats. It describes some conventional and emerging novel techniques that could be applied for the detection of microbial and chemical hazards in cultured meat. The review also outlines the current evolving regulatory landscape around cultured meat and explains how thorough detection and characterization of microbiological and chemical hazards through advanced analytical techniques can provide crucial data to help develop robust, evidence-based food safety regulations specifically tailored for the cultured meat industry. Implementing new digital food safety methods is recommended for further research on the sensitive and effective detection of microbiological and chemical hazards in cultured meat.

Practical Online Characterization of the Properties of Hydrocracking Bottom Oil via Near-Infrared Spectroscopy

Providing real-time information on the chemical properties of hydrocracking bottom oil (HBO) as the feedstock for ethylene cracker while minimizing processing time, is important to improve the real-time optimization of ethylene production. In this study, a novel approach for estimating the properties of HBO samples was developed on the basis of near-infrared (NIR) spectra. The main noise and extreme samples in the spectral data were removed by combining discrete wavelet transform with principal component analysis and Hotelling’s T2 test. Kernel partial least squares (KPLS) regression was utilized to account for the nonlinearities between NIR data and the chemical properties of HBO. Compared with the principal component regression, partial least squares regression, and artificial neural network, the KPLS model had a better performance of obtaining acceptable values of root mean square error of prediction (RMSEP) and mean absolute relative error (MARE). All RMSEP and MARE values of density, Bureau of Mines correlation index, paraffins, isoparaffins, and naphthenes were less than 1.0 and 3.0, respectively. The accuracy of the industrial NIR online measurement system during consecutive running periods in predicting the chemical properties of HBO was satisfactory. The yield of high value-added products increased by 0.26 percentage points and coil outlet temperature decreased by 0.25 °C, which promoted economic benefits of the ethylene cracking process and boosted industrial reform from automation to digitization and intelligence.

Detection of Soluble Solids Content in Different Cultivated Fresh Jujubes Based on Variable Optimization and Model Update

To solve the failure problem of the visible/near infrared (VIS/NIR) spectroscopy model, soluble solids content (SSC) detection for fresh jujubes cultivated in different modes was carried out based on the method of variable optimization and model update. Iteratively retained informative variables (IRIV) and successive projections algorithm (SPA) algorithms were used to extract characteristic wavelengths, and least square support vector machine (LS-SVM) was used to establish detection models. Compared with IRIV, IRIV-SPA achieved better performance. Combined with the offset properties of the wavelength, repeated wavelengths were removed, and wavelength recombination was carried out to create a new combination of variables. Using these fused wavelengths, the model was recalibrated based on the Euclidean distance between samples. The LS-SVM detection model of SSC was established using the update method of wavelength fusion-Euclidean distance. Good prediction results were achieved using the proposed model. The determination coefficient (R²), root mean square error (RMSE), and residual predictive deviation (RPD) of the test set on SSC of fresh jujubes cultivated in the open field were 0.82, 1.49%, and 2.18, respectively. The R², RMSE, and RPD of the test set on SSC of fresh jujubes cultivated in the rain shelter were 0.81, 1.44%, and 2.17, respectively. This study realized the SSC detection of fresh jujubes with different cultivation and provided a method for the establishment of a robust VIS/NIR detection model for fruit quality, effectively addressing the industry need for identifying jujubes grown in the open field.

Unveiling non-linear water effects in near infrared spectroscopy: A study on organic wastes during drying using chemometrics

In the context of organic waste management, near infrared spectroscopy (NIRS) is being used to offer a fast, non-destructive, and cost-effective characterization system. However, cumbersome freeze-drying steps of the samples are required to avoid water's interference on near infrared spectra. In order to better understand these effects, spectral variations induced by dry matter content variations were obtained for a wide variety of organic substrates. This was made possible by the development of a customized near infrared acquisition system with dynamic highly-resolved simultaneous scanning of near infrared spectra and estimation of dry matter content during a drying process at ambient temperature. Using principal components analysis, the complex water effects on near infrared spectra are detailed. Water effects are shown to be a combination of both physical and chemical effects, and depend on both the characteristics of the samples (biochemical type and physical structure) and the moisture content level. This results in a non-linear relationship between the measured signal and the analytical characteristic of interest. A typology of substrates with respect to these water effects is provided and could further be efficiently used as a basis for the development of local quantitative calibration models and correction methods accounting for these water effects.

Near infrared system coupled chemometric algorithms for the variable selection and prediction of baicalin in three different processes

Characteristic variables are essential and necessary basis in model construction, and are related to the prediction result closely in near infrared spectroscopy (NIRS) analysis. However, the same compound usually has different characteristic variables for different analysis and it would be lower correlation between variables and structure in many researches. So, the accuracy and reliability are expected to improve by exploring characteristic variables in different spectrum analysis. In this study, competitive adaptive weighted resampling method (CARS) was applied to select characteristic variables related to baicalin from NIRS analysis data, which were applied to analysis of baicalin in three different processes including the herb, extraction process and concentration process of Scutellaria baicalensis. After application of CARS method, 70, 50 and 50 variables were selected respectively from three processes above. The selected variables were firstly analyzed by statistical methods that they were found to be consistent and correlated among three different processes after one-way analysis of variance test and Kendall's W. Partial least-squares (PLS) regression and extreme learning machine (ELM) models were constructed based on optimized data. Models after variable selection were less complicated and had better prediction results than global models. After comparison, CARS-PLS was suitable for the prediction of extraction process, while for the concentration process and herb, CARS-ELM performed better. The Rc value of the herb, extraction and concentration model were 0.9469, 0.9841 and 0.9675, respectively. The RSEP values were 4.54%, 6.96% and 8.37%, respectively. The results help to frame a theoretical basis for characteristic variables of baicalin.