Quantitative analysis of moisture content and particle size in a fluidized bed granulation process using near infrared spectroscopy and acoustic emission combined with data fusion strategies

Rapid analysis of methanol content in methanol–gasoline is of great significance to monitor the methanol–gasoline quality. In this work, two different data-fusion strategies based on Raman and near-infrared (NIR) spectroscopies coupled with partial least square (PLS) were constructed and applied for a rapid and accurate analysis of the methanol content in methanol–gasoline. The Raman and NIR spectra of 49 methanol–gasoline samples were recorded, and the characteristic peaks of the methanol–gasoline samples in Raman and NIR spectroscopies were identified. For spectral data fusion, two different data-fusion strategies based on Raman and NIR spectroscopies coupled with PLS were proposed; normalization was used for low-level data fusion, and variable importance in projection (VIP) was used for mid-level data fusion. The different spectra pretreatment methods, latent variables, and variable importance thresholds of VIP were explored and optimized by 5-fold cross-validation (CV) to optimize the PLS calibration model for methanol content analysis. To further prove the predictive performance and stability of the PLS calibration model based on two data-fusion strategies, four PLS calibration models based on Raman, NIR, and two data-fusion strategies were applied to the quantitative analysis of methanol content in methanol–gasoline. The results show that the predictive performance of PLS calibration models based on the two data-fusion strategies is improved, and the PLS calibration model based on mid-level data fusion strategy gave an excellent predictive performance in methanol content analysis, with coefficients of determination of cross-validation (Rcᵥ²) and validation set (Rᵥ²) of 0.9988 and 0.9905, respectively, and root mean square error of cross-validation (RMSECV) and validation set (RMSEV) of 0.0068 and 0.0288%, respectively. Therefore, data fusion based on Raman and NIR spectroscopies coupled with PLS can give a rapid and accurate quantitative analysis of the methanol content in methanol–gasoline.

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A novel CNN-LSTM model with attention mechanism for online monitoring of moisture content in fluidized bed granulation process based on near-infrared spectroscopy.

The effect of two drying methods (oven and freeze drying) and the addition of maltodextrin to Kakadu plum puree samples (KP) (Terminalia ferdianandiana) were evaluated using mid (MIR) and near-infrared (NIR) spectroscopy. Dry powder samples were obtained using the oven and freeze-drying methods and seven levels of maltodextrin. Training (n = 32) and validation (n = 28) sets were developed for the prediction of moisture (M %), water activity (aw %), hydroxymethylfurfural (HMF) and vitamin C (VITC mg/100 g DM) based on NIR and MIR spectroscopy. Results from this study demonstrated the ability of spectroscopy combined with partial least squares (PLS) regression to monitor these parameters during drying. The standard error in cross validation (SECV) and the residual predictive deviation (RPD) values obtained were of 0.71% (RPD = 4.1) and 0.47% (RPD = 6.1) for M, 0.06% (RPD = 4.4) and 0.02% (RPD = 8.2) for aw, 0.73 (RPD = 3.3) and 0.72 (RPD = 3.3) for HMF, 465.7 mg 100 g DM (RPD = 3.0) and 289.3 mg 100 g DM (RPD = 4.8) for VITC, using MIR and NIR, respectively. The results from this study showed that MIR and NIR spectroscopies are capable of both measuring and monitoring the effect of drying and the addition of maltodextrin as a carrier to KP puree samples.

Accuracy of near infrared spectroscopy for prediction of chemical composition, salt content and free amino acids in dry-cured ham

Almond kernels show large variability for oil content and fatty acid profile. The objective of this research was to evaluate the potential of near infrared (NIR) reflectance spectroscopy (NIRS) for the analysis of these traits in almond flour. Ground kernels of 181 accessions collected in 2009 were used for developing calibration equations for oil content and concentrations of individual fatty acids. Calibration equations were developed using second derivative transformation and modified partial least squares regression. They were validated with samples from 179 accessions collected in 2010. The accuracy of calibration equations was measured through the coefficient of determination (r2) in external validation and the ratio of the SD in the validation set to the standard error of prediction (RPD). Both r2 and RPD were high for oil content (r2 = 0.99; RPD = 9.24) and concentrations of oleic (r2 = 0.97; RPD = 5.37) and linoleic acids (r2 = 0.98; RPD = 7.35), revealing that calibration equations for these traits are highly accurate. Conversely, the accuracy of the calibration equations for palmitic (r2 = 0.54; RPD = 1.41) and stearic acids (r2 = 0.52; RPD = 1.44) was too low for allowing their application in practice. NIRS discrimination of oil content and concentrations of oleic and linoleic acids was mainly based on the spectral region from 2240 to 2380 nm.Practical applications: NIRS is a high‐throughput analytical technique that allows fast measurement of several traits in a single analysis without using chemical reagents. We evaluated the feasibility of analyzing oil content and concentrations of palmitic, stearic, oleic, and linoleic acids in almond flour using fruits collected during 2 years from a world germplasm collection. The fruits collected in 2009 were used for NIRS calibration, whereas the fruits collected in 2010 were used for validation. NIRS equations were highly accurate for measuring oil content and concentrations of oleic and linoleic acids, which are important traits defining the quality of almond flour for specific uses in the food industry. These results have applications both in the research laboratory and the food industry, where NIRS is becoming a widely used technique for quality control.

A data fusion method based on near infrared (NIR) spectra and ultraviolet (UV) spectra for simultaneous determination of six ginsenosides and four saccharides in Chinese herbal injection (CHI) was developed. Two data fusion strategies (low-level data fusion and mid-level data fusion) combined with partial least squares (PLS) regression and uninformative variable elimination by PLS (UVEPLS) regression were implemented, respectively. Compared with the models established by independent NIR or UV spectra, there was a significant improvement provided by two data fusion strategies, which benefited from the synergistic effect of complementary information obtained from near infrared spectroscopy (NIRS) and ultraviolet spectroscopy (UVS). The results in this work showed data fusion of NIR and UV spectra combined with a regression algorithm could be a promising strategy to determine the ginsenosides and saccharides in CHI rapidly and simultaneously.

As an essential index to evaluate feed quality, feed moisture content which is too high or too low will impose an adverse impact on feed nutritional value. Therefore, the quantitative analysis of feed moisture content is significant. In this paper, the detection of feed moisture content based on terahertz (THz) and near-infrared (NIR) spectroscopy and data fusion technology of THz and NIR (THz-NIR) was investigated. First, feed samples with different water content (29.46%–49.46%) were prepared, and THz (50–3000 μm) and NIR (900–1700 nm) spectral data of samples was collected and preprocessed, and the feed samples were divided into correction set and verification set by 2:1. Second, the spectral data was fused through the head-to-tail splicing, and the feed moisture content prediction model was established combined with partial least squares regression (PLSR). Third, competitive adaptive reweighting sampling (CARS) was applied to extract spectral characteristic variables for feature layer fusion, and the feed moisture content prediction model in feature level was constructed combined with PLSR. Finally, the evaluation parameters validation set correlation coefficient (Rp), the root mean square error of prediction (RMSEP), and the residual predictive deviation (RPD) were employed to evaluate the prediction effect of the model. The results indicated that THz, NIR spectra, and data fusion technology could quickly and effectively predict feed moisture content. Among them, the characteristic layer spectral data fusion model achieved the optimal prediction effect while Rp, RMSEP, and RPD reached 0.9933, 0.0069, and 8.7386 respectively. In conclusion, compared with the prediction model established by single THz and NIR spectrum, THz-NIR spectrum data fusion could more accurately predict feed moisture content and provide certain theoretical and technical support for inspirations and methods for quantitative analysis of feed moisture content of livestock and poultry.

To meet consumer demand for high-quality fruit and replace traditional subjective assessment methods, there is a growing interest in objective, quantitative, and non-destructive testing techniques within the agricultural and food industries. This study explores the integration of near-infrared (NIR) spectroscopy with machine learning for the quality detection of apricot–plum hybrids, aiming to provide a rapid and efficient technical approach. Two cultivars, ‘Fengweimeigui’ and ‘Weidi’, were selected for analysis. The relationships between various quality attributes were analyzed using analysis of variance (ANOVA) and Pearson correlation. Raw spectral data were preprocessed using Savitzky–Golay (SG) smoothing, and principal component analysis (PCA) was employed to reduce the high dimensionality of the spectral data. The scores of the first 15 principal components (PCs) were extracted as input features for the subsequent models. A comparative study was conducted between backpropagation neural network (BPNN) and support vector machine (SVM) models. The results indicated that during the color-break period, significant differences existed across all quality indicators except for dry matter content, with significant correlations observed among these parameters. The results demonstrated that BPNN achieved the best predictive performance for total phenols content, peel L*, peel b*, vitamin C content, flavonoids content, soluble solids content, soluble sugars content, and soluble protein content in ‘Weidi’ and ‘Fengweimeigui’ from the color-turning to the ripening stages. The RP2 values for these indicators were 0.968, 0.966, 0.950, 0.939, 0.939, 0.923, 0.921, and 0.905, respectively, with residual predictive deviation (RPD) values exceeding 3.0. These findings indicate that near-infrared (NIR) spectroscopy is a feasible tool for the rapid detection of plum–apricot quality. However, the model performance for Flesh a* requires further optimization. In conclusion, the combination of NIR spectroscopy and machine learning enables the rapid, efficient, and non-destructive quality assessment of plum–apricot hybrids, providing robust technical support for maturity prediction and quality control in commercial production.

Quantitative analysis of caprolactam in sauce-based food using infrared spectroscopy combined with data fusion strategies

Application of a data fusion strategy combined with multivariate statistical analysis for quantification of puerarin in Radix puerariae

Near-infrared (NIR) spectroscopy (NIRS) is a widely accepted method of measuring moisture in pharmaceutical freeze-dried products, both during the process and in the finished products. Multiple NIR measurement approaches have been introduced to monitor product moisture in freeze-dried vials. However, the spatial moisture gradients within a vial have not been investigated in depth. Like any other point-focused process analytical technology (PAT) tool, a spectrum produced by NIRS represents an average over a given area of the product vial. Implementing a point-focused NIR on any random position without proper understanding of spatial moisture variations within the vial may severely impact the reliability of the results. The present work focuses on understanding the moisture distribution within freeze-dried vials. We performed an investigation using NIR tools, NIR chemical imaging (NIR-CI), and NIRS to understand the spatial variations in moisture on the outer surface (i.e., periphery) of the freeze-dried vials. To achieve this, the moisture distribution within individual vials was mapped using NIR images. Then, NIRS was used to determine the necessity of using multiple measurement points to produce robust models quantifying the moisture inside freeze-dried products. Overall, the results show a simplified version of the phenomenon in which non-homogenous distribution of moisture, as well as the non-uniform drying front, occur within the vials. The findings from the NIRS-based partial least squares (PLS) models indicate that to achieve reliable product/process information, measurements must be drawn from multiple measurement points on the surface of the freeze-dried products.

OBJECTIVE: To study and establish quality con-trol model of the Salvianolic Acid B by Near In-frared Spectroscopy (NIRS), and to realize on-line quality control of extracting and purifying proc-esses of industrial scale herbal product manu-facturing. METHOD: NIR chromatography was obtained from on-line NIR detection of extract-ing process and purifying process. HPLC analysis was carried out to determine the con-tents of salvianolic acid B. Partial Least Squares Regression (PLS) was used to establish the model between the information between NIRS and HPLC. RESULTS: For extracting model: the optimum Near Infrared (NIR) wavelength range was 9815- 5430cm-1, R=0.9784, RMSEC=0.258; for puri-fying model: the optimum NIR wavelength range was 9815-5430cm-1, R=0.9776, RMSEC=4.02. The average relative error was <5%. CONCLUSION: NIR technique is applicable for on-line quality control in production of salvianolic acid B.

NIRS to assess chemical composition of sheep and goat cheese

The non-invasive assessment of avocado maturity and quality