Search Results (528)

Wine quality is closely linked to sensory attributes such as aroma, taste, and mouthfeel, all of which are influenced by grape variety, “terroir”, and vinification practices. Among these, aroma is particularly important for consumer preference, and it results from a complex interplay of numerous volatile compounds. Conventional sensory methods, such as descriptive analysis (DA) performed by trained panels, offer valuable insights but are often time-consuming, resource-intensive, and subject to individual variability. Recent advances in sensor technologies—including electronic nose (E-nose) and electronic tongue (E-tongue)—combined with chemometric techniques and machine learning algorithms, offer more efficient, objective, and predictive approaches to wine aroma profiling. These tools integrate analytical and sensory data to predict aromatic characteristics and quality traits across diverse wine styles. Complementary techniques, including gas chromatography (GC), near-infrared (NIR) spectroscopy, and quantitative structure–odor relationship (QSOR) modeling, when integrated with multivariate statistical methods such as partial least squares regression (PLSR) and neural networks, have shown high predictive accuracy in assessing wine aroma and quality. Such approaches facilitate real-time monitoring, strengthen quality control, and support informed decision-making in enology. However, aligning instrumental outputs with human sensory perception remains a challenge, highlighting the need for further refinement of hybrid models. This review highlights the emerging role of predictive modeling and sensor-based technologies in advancing wine aroma evaluation and quality management. Full article

The black soil region of northeast China is a critical global grain production base. The dynamic variations in soil organic carbon (SOC) are directly linked to the regional food security. To accurately monitor SOC content and evaluate the potential of integrating Landsat-9 and GF-1 satellite data for SOC inversion, we developed a machine learning framework that combines data from both satellite sources to model SOC. Using the typical black soil region of northeast China in the Tongken River Basin as the study area, we compared the MLR, PLSR, RF, and XGBoost algorithms. And XGBoost demonstrated the highest performance (R² = 0.9130; RMSE = 0.3834%). Based on the optimal model, SOC in the study area was projected from 2020 to 2024. The multi-year average SOC exhibited an initial increase followed by a subsequent decline, with an overall increase of 22.78%. Spearman correlation analysis identified parent material as the dominant factor controlling SOC variation at the watershed scale (correlation coefficient = 0.38) while also modulating the influence of land use types on SOC dynamics. The “space–ground” multi-source collaborative inversion framework developed in this study offers a high-precision technical approach for the monitoring of SOC in black soil regions. Full article

To reveal the characteristic physicochemical indicators of wines of different quality grades and explore their feasibility as auxiliary indicators for grading, 23 wines from the Manas subregion of Xinjiang were used as test materials. Sensory evaluation, colour difference analysis, and electronic tongue technology were employed, combined with nontargeted metabolomics and quantitative analysis, to analyze differences in phenolic compounds, colour parameters, and taste characteristics among wines of different grades. Finally, a quality evaluation model for Cabernet Sauvignon wine was constructed using partial least squares regression (PLSR). The results revealed significant differences in the L* values, a* values, and C*ab values among wines of different grades. Grade A wines presented lower L* values, higher a* values, and higher C*ab values, indicating lower brightness, deeper red tones, and higher saturation. Taste characteristic differences were primarily manifested in Grade A wines, which have higher acidity, astringency, bitterness, and richness but exhibit lower bitterness aftertaste and astringency aftertaste. The results of the quantitative analysis and correlation analysis indicate that the differences in sensory characteristics among different grades of wine stem from variations in their polyphenolic compound contents. The higher anthocyanin content in Grade A wine is associated with higher a* values; higher flavonoid content is closely related to higher astringency and bitterness values; and lower flavanol content is associated with lower bitterness aftertaste and astringency aftertaste values. The PLSR model results indicate that when sensory characteristic parameters and phenolic compound content are used as predictor variables (X) and grade is used as the response variable (Y), the PLSR model has a calibration set R² = 0.97 and a validation set R² = 0.92, the calibration set RMSE is 0.13, and the validation set RMSE is 0.25. The model demonstrates good fitting performance, establishing an objective method for evaluating wine quality that avoids evaluation errors caused by the subjective factors of winemakers and tasters. This study is the first to conduct a comprehensive evaluation of the sensory characteristic and chemical components of three grades of wine, providing data support and theoretical references for the improvement of wine quality evaluation systems. Full article

Rapid and accurate assessment of nutritional quality, particularly crude protein content and essential nutrient concentrations, remains a major challenge in the food and feed industries. In this study, laser-induced breakdown spectroscopy (LIBS) was combined with advanced chemometric modeling to predict the levels of crude protein and key macro- and micronutrients (Ca, Mg, K, Na, Fe, Mn, P, Zn) in 61 barley forage samples composed of whole aerial plant parts ground prior to analysis. LIBS offers a compelling alternative to traditional analytical methods by enabling real-time analysis with minimal sample preparation. To minimize interference from atmospheric nitrogen, nitrogen spectral lines were excluded from the protein calibration model in favor of spectral lines from elements biochemically associated with proteins. We compared the performance of Partial Least Squares (PLSR) regression and Extreme Learning Machine (ELM) using fivefold cross-validation. ELM outperformed PLS in terms of prediction, achieving a coefficient of determination (R²) close to 1 and a ratio of performance to deviation (RPD) exceeding 2.5 for proteins and several nutrients. These results underscore the potential of LIBS-ELM integration as a robust, non-destructive, and in situ tool for rapid forage quality assessment, particularly in complex and heterogeneous plant matrices. Full article

Neuronal differentiation is a highly dynamic process marked by coordinated biochemical, structural, and metabolic changes. Rat primary cortical neurons are the preferred cell model to study this process as they can maintain their functional attributes, including functional synapses, and simulate the behavior of neuronal cells in vivo. In this study, we employed Fourier transform infrared (FTIR) spectroscopy to monitor the molecular transformations that occur during the differentiation of rat cortical neurons. Partial least squares regression (PLS-R) analysis from the 1800–1500 cm⁻¹ region further allows the identification of the spectroscopic profile of early and late differentiation stages, highlighting the technique’s ability to detect subtle molecular changes. Further peak intensity analysis revealed significant changes in the cells’ metabolome during differentiation; it was possible to observe remodeling of protein secondary structures and an increase in protein phosphorylation levels, which can imply activation of signaling pathways essential for neuronal differentiation and maturation. Concomitantly, lipid-associated spectral regions demonstrated increased levels of total lipids, lipid esters, and longer acyl chains and decreased unsaturation levels, alterations that can be linked to membrane expansion throughout neuronal differentiation. These findings underscore FTIR spectroscopy as a valuable tool for studying neuronal differentiation, offering insights into the conformational and metabolic shifts underlying the formation of mature neuronal phenotypes. Full article

Efficient and convenient intelligent online detection methods can provide important technical support for the standardization of processing flow in the tea industry. Hence, this study focuses on the key chemical indicators—tea pigments in the rolling process of black tea as the research object, and uses multi-source information fusion methods to predict the changes of tea pigments content. Firstly, the tea pigments content of the samples under different rolling time series of black tea is determined by system analysis methods. Secondly, the spectra and images of the corresponding samples under different rolling time series are simultaneously obtained through the portable near-infrared spectrometer and the machine vision system. Then, by extracting the principal components of the image feature information and screening characteristic wavelengths from the spectral information, low-level and middle-level data fusion strategies are chosen to effectively integrate sensor data from different sources. At last, the linear (PLSR) and nonlinear (SVR and LSSVR) models are established respectively based on the different characteristic data information. The research results show that the LSSVR based on middle-level data fusion strategy have the best effect. In the prediction results of theaflavins, thearubigins, and theabrownins, the correlation coefficients of the testing sets are all greater than 0.98, and the relative percentage deviations are all greater than 5. The complementary fusion of the spectrum and image information effectively compensates for the problems of information redundancy and feature missing in the quantitative analysis of tea pigments content using the single-modal data information. Full article

Stable crop yields require an appropriate supply of essential soil nutrients such as nitrogen (N), phosphorus (P), and potassium (K) based on the accurate diagnosis of soil nutrient status. Traditional laboratory analysis of soil nutrients is often complicated and time-consuming and does not provide real-time nutrient status. Visible–near-infrared (Vis-NIR) spectroscopy has emerged as a non-destructive and rapid method for estimating soil nutrient levels. Vis-NIR spectra reflect sample characteristics as the peak intensities; however, they are often affected by various artifacts and complex variables. Since Vis-NIR spectroscopy does not directly measure nutrient levels in soil, improving estimation accuracy is essential. For spectral preprocessing, the most important aspect is to develop an appropriate preprocessing strategy based on the characteristics of the data and identify artifacts such as noise, baseline drift, and scatter in the spectral data. Machine learning-based modeling techniques such as partial least-squares regression (PLSR) and support vector machine regression (SVMR) enhance estimation accuracy by capturing complex patterns of spectral data. Therefore, this review focuses on the use of Vis-NIR spectroscopy for evaluating soil properties including soil water content, organic carbon (C), and nutrients and explores its potential for real-time field application through spectral preprocessing and machine learning algorithms. Vis-NIR spectroscopy combined with machine learning is expected to enable more efficient and site-specific nutrient management, thereby contributing to sustainable agricultural practices. Full article

The acid number evaluates the degree of deterioration of lubricating oil. Existing methods for evaluating the performance degradation of lubricating oils are mostly based on the detection of traditional physical and chemical indicators, which often only reflect a single dimension of the degradation process, thus affecting the accuracy and repeatability of the results. Integrating multi-dimensional information can more comprehensively reflect the essence of degradation, which can improve the accuracy and reliability of the evaluation results. Mid-infrared spectroscopy is an effective means of monitoring the acid number. In this study, a combination of infrared spectroscopy quantitative analysis and chemometrics was used. The oil sample data was divided into training set and validation set by the Kennard–Stone method. In the experiment, a Fourier transform infrared spectrometer equipped with an attenuated total reflection accessory (ATR-FTIR) was used to collect spectral data of the samples in the wavenumber range of 1750–1700 cm⁻¹ (this range corresponds to the characteristic absorption of carboxyl groups and is directly related to the acid number). Meanwhile, a G20S automatic potentiometric titrator was used to determine the acid number as a reference value in accordance with GB/T 7304. The study compared various preprocessing methods. A regression prediction model between the spectra and acid number was established using partial least squares regression (PLSR) within the selected wavenumber range, with the root mean square error of cross-validation (RMSECV), root mean square error of prediction (RMSEP), and coefficient of determination (R) as evaluation indicators. The experimental results showed that the PLSR model established after preprocessing with second derivative combined with seven-point smoothing exhibited the optimal performance, with an RMSECV of 0.00505, an RMSEP of 0.14%, and an R of 0.9820. Compared with the traditional titration method, this prediction method is more suitable for real-time monitoring of production lines or rapid on-site screening of equipment. It can in a timely manner warn of the deterioration trend of lubricating oil, reduce the risk of equipment wear caused by oil failure, and provide efficient technical support for lubricating oil life management. Full article

Cucumber, a high-yielding crop commonly grown in facility environments, is particularly susceptible to nitrogen (N) deficiency due to its rapid growth and high nutrient demand. This study used cucumber as its experimental subject and established a spectral dataset of leaves under four nutritional conditions, normal supply, nitrogen deficiency, phosphorus deficiency, and potassium deficiency, aiming to develop an efficient and robust method for quantifying N in cucumber leaves using Raman spectroscopy (RS). Spectral data were preprocessed using three baseline correction methods—BaselineWavelet (BW), Iteratively Improve the Moving Average (IIMA), and Iterative Polynomial Fitting (IPF)—and key spectral variables were selected using 4-Dimensional Feature Extraction (4DFE) and Competitive Adaptive Reweighted Sampling (CARS). These selected features were then used to develop a N content prediction model based on Partial Least Squares Regression (PLSR). The results indicated that baseline correction significantly enhanced model performance, with three methods outperforming unprocessed spectra. A further analysis showed that the combination of IPF, 4DFE, and CARS achieved optimal PLSR model performance, achieving determination coefficients (R²) of 0.947 and 0.847 for the calibration and prediction sets, respectively. The corresponding root mean square errors (RMSEC and RMSEP) were 0.250 and 0.368, while the residual predictive deviation (RPDC and RPDP) values reached 4.335 and 2.555. These findings confirm the feasibility of integrating RS with advanced data processing for rapid, non-destructive nitrogen assessment in cucumber leaves, offering a valuable tool for nutrient monitoring in precision agriculture. Full article

This study aims to enhance the performance of an innovative steel slag–ceramsite foam concrete (SSCFC) to advance sustainable green building materials. An eco-friendly composite construction material was developed by integrating industrial by-product steel slag (SS) with lightweight ceramsite. Employing a three-factor, three-level orthogonal experimental design at a fixed density of 800 kg/m³, 12 mix proportions (including a control group) were investigated with the variables of water-to-cement (W/C) ratio, steel slag replacement ratio, and ceramsite replacement ratio. The governing mechanisms of the W/C ratio, steel slag replacement level, and ceramsite replacement proportion on the SSCFC’s fluidity and compressive strength (CS) were elucidated. The synergistic application of range analysis and analysis of variance (ANOVA) quantified the significance of factors on target properties, and partial least squares regression (PLSR)-based prediction models were established. The test results indicated the following significance hierarchy: steel slag replacement > W/C ratio > ceramsite replacement for fluidity. In contrast, W/C ratio > ceramsite replacement > steel slag replacement governed the compressive strength. Verification showed R² values exceeding 65% for both fluidity and CS predictions versus experimental data, confirming model reliability. Multi-criteria optimization yielded optimal compressive performance and suitable fluidity at a W/C ratio of 0.4, 10% steel slag replacement, and 25% ceramsite replacement. Full article

Due to consumer needs and the prevalence of gluten-related disorders such as celiac disease, the gluten-free food market is expanding rapidly and is expected to surpass USD 2.4 billion by 2036. The objective of this study was to substitute wheat flour with oat, black rice, brown rice, buckwheat, and rice flours in the production of gluten-free scones, to assess consumer acceptability, and to identify factors contributing to consumer acceptability using check-all-that-apply questions. The 10 attributes of appearance, color, texture, grainy flavor, sweetness, familiar flavor, novelty, familiarity, moistness, and consistency exhibited statistically significant differences among the samples (p < 0.001). One hundred consumers evaluated 18 attributes using a nine-point hedonic scale, and all attributes demonstrated statistically significant differences across six samples (p < 0.001). The samples from buckwheat and wheat scored the highest in consumer acceptability. The results indicate a strong positive correlation between overall liking and purchase intention, with sensory attributes such as nutty flavor, cohesiveness, appearance, moistness, color, texture, and inner softness positively influencing consumer acceptability. The attributes affecting negatively were thick throat sensation, unique flavor, and stuffiness. This study is expected to provide data to aid in the development of better-tasting gluten-free products that meet customer and market needs. Full article

Vibrational spectroscopy, including Raman and near-infrared techniques, enables the non-destructive evaluation of starch gelatinization, head rice yield, and aroma-active volatile compounds in parboiled rice subjected to varying soaking and drying conditions. Raman and NIR spectra were collected for rice samples processed under different conditions and integrated with reference analyses to develop and validate partial least squares regression and artificial neural network models. The optimized PLSR model demonstrated strong predictive performance, with R² values of 0.9406 and 0.9365 for SG and HRY, respectively, and residual predictive deviations of 3.98 and 3.75 using Raman effective wavelengths. ANN models reached R² values of 0.97 for both SG and HRY, with RPDs exceeding 4.2 using NIR effective wavelengths. In the aroma compound analysis, p-Cymene exhibited the highest predictive accuracy, with R² values of 0.9916 for calibration, and 0.9814 for cross-validation. Other volatiles, such as 1-Octen-3-ol, nonanal, benzaldehyde, and limonene, demonstrated high predictive reliability (R² ≥ 0.93; RPD > 3.0). Conversely, farnesene, menthol, and menthone showed poor predictability (R² < 0.15; RPD < 0.4). Principal component analysis revealed that the first principal component explained 90% of the total variance in the Raman dataset and 71% in the NIR dataset. Hotelling’s T² analysis identifies influential outliers and enhances model robustness. Optimal processing conditions for achieving maximum HRY and SG values were determined at 65 °C soaking for 180 min, followed by drying at 70 °C. This study underscores the potential of integrating vibrational spectroscopy with machine learning techniques and targeted wavelength selection for the high-throughput, accurate, and scalable quality evaluation of parboiled rice. Full article

Black garlic is a thermally processed product derived from fresh garlic through controlled high-temperature and -humidity conditions. During this process, the formation of 5-hydroxymethylfurfural (5-HMF), a potentially harmful byproduct, is a major quality and safety concern in food processing. This study systematically investigated the distributions of 5-HMF and key process-related biochemical components in black garlic samples from three major production regions in China—Jiangsu, Yunnan, and Shandong. Additionally, correlations between 5-HMF and biochemical components—reducing sugars, amino acids, and organic acids—were analyzed to inform process optimization strategies. Results showed significant regional variation in 5-HMF content, with Jiangsu black garlic exhibiting the highest levels, followed by Yunnan and Shandong (p < 0.05). Partial least squares regression analysis (PLSR) indicated that the key biochemical factors regulating 5-HMF accumulation are primarily organic acids. Among them, citric acid was identified as the most important negative regulator (VIP = 3.11). Although acetic acid (VIP = 1.38) and malic acid (VIP = 1.03) showed positive correlations with 5-HMF, aspartic acid (VIP = 0.41) and fructose (VIP = 0.43) exhibited a weak positive correlation, and arginine (VIP = 0.89) showed weak negative correlations, their effects were far less significant than that of citric acid. Based on these findings, we propose a potential strategy for reducing 5-HMF content in black garlic—selecting raw material cultivars with higher endogenous citric acid levels or exploring the exogenous addition and regulation of citric acid during processing. This study provides a theoretical foundation for understanding the accumulation mechanism of 5-HMF in black garlic and suggests new potential regulatory directions for controlling its content. Full article

Soil organic carbon (SOC) is a critical indicator of soil health and carbon sequestration potential. Accurate, efficient, and scalable SOC estimation is essential for sustainable orchard management and climate-resilient agriculture. However, traditional visible–near-infrared (Vis–NIR) spectroscopy often suffers from limited chemical specificity and weak adaptability in heterogeneous soil environments. To overcome these limitations, this study develops a five-stage modeling framework that systematically integrates Fourier Transform Infrared (FTIR) spectroscopy with hybrid machine learning techniques for non-destructive SOC prediction in citrus orchard soils. The proposed framework includes (1) FTIR spectral acquisition; (2) a comparative evaluation of nine spectral preprocessing techniques; (3) dimensionality reduction via three representative feature selection algorithms, namely the Successive Projections Algorithm (SPA), Competitive Adaptive Reweighted Sampling (CARS), and Principal Component Analysis (PCA); (4) regression modeling using six machine learning algorithms, namely the Random Forest (RF), Support Vector Regression (SVR), Gray Wolf Optimized SVR (SVR-GWO), Partial Least Squares Regression (PLSR), Principal Component Regression (PCR), and the Back-propagation Neural Network (BPNN); and (5) comprehensive performance assessments and the identification of the optimal modeling pathway. The results showed that second-derivative (SD) preprocessing significantly enhanced the spectral signal-to-noise ratio. Among feature selection methods, the SPA reduced over 300 spectral bands to 10 informative wavelengths, enabling efficient modeling with minimal information loss. The SD + SPA + RF pipeline achieved the highest prediction performance (R² = 0.84, RMSE = 4.67 g/kg, and RPD = 2.51), outperforming the PLSR and BPNN models. This study presents a reproducible and scalable FTIR-based modeling strategy for SOC estimation in orchard soils. Its adaptive preprocessing, effective variable selection, and ensemble learning integration offer a robust solution for real-time, cost-effective, and transferable carbon monitoring, advancing precision soil sensing in orchard ecosystems. Full article

Milk jujube has become an increasingly popular tropical fruit. The sugar content, which is commonly represented by the soluble solid content (SSC), is a key indicator of the flavor, internal quality, and market value of milk jujubes. Traditional methods for assessing SSC are time-consuming, labor-intensive, and destructive. These methods fail to meet the practical demands of the fruit market. A rapid, stable, and effective non-destructive detection method based on visible/near-infrared (Vis/NIR) spectroscopy is proposed here. A Vis/NIR reflectance spectroscopy system covering 340–1031 nm was constructed to detect SSC in milk jujubes. A structured spectral modeling framework was adopted, consisting of outlier elimination, dataset partitioning, spectral preprocessing, feature selection, and model construction. Comparative experiments were conducted at each step of the framework. Special emphasis was placed on the impact of outlier detection and dataset partitioning strategies on modeling accuracy. A data-augmentation-based unsupervised anomaly sample elimination (DAUASE) strategy was proposed to enhance the data validity. Multiple data partitioning strategies were evaluated, including random selection (RS), Kennard–Stone (KS), and SPXY methods. The KS method achieved the best preservation of the original data distribution, improving the model generalization. Several spectral preprocessing and feature selection methods were used to enhance the modeling performance. Regression models, including support vector regression (SVR), partial least squares regression (PLSR), multiple linear regression (MLR), and backpropagation neural network (BP), were compared. Based on a comprehensive analysis of the above results, the DAUASE + KS + SG + SNV + CARS + SVR model exhibited the highest prediction performance. Specifically, it achieved an average precision (AP_p) of 99.042% on the prediction set, a high coefficient of determination (R_P²) of 0.976, and a low root-mean-square error of prediction (RMSEP) of 0.153. These results indicate that Vis/NIR spectroscopy is highly effective and reliable for the rapid and non-destructive detection of SSC in milk jujubes, and it may also provide a theoretical basis for the practical application of rapid and non-destructive detection in milk jujubes and other jujube varieties. Full article