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The moisture content (MC) and acidity content (AC) of the fermented grains used in liquor production directly affect the liquor quality and yield; as such, they are important indicators used to evaluate the quality of fermented grains. In this study, extreme gradient enhancement algorithm (XGBoost), partial least square regression (PLSR), and extreme learning machine (ELM) models were developed based on spectral data collected by near-infrared (NIR) hyperspectral imaging (HSI) technology. First, PLSR models were established after SNV and MSC algorithms preprocessed the HSI data, and the best preprocessing method was determined (MC: SNV; AC: MSC). Then, the competitive adaptive reweighting sampling (CARS) algorithm and principal component analysis (PCA), both combined with the successive projection algorithm (SPA), were used to extract the characteristic wavelengths from the full-band spectral data. Ultimately, the XGBoost model developed using the characteristic wavelengths extracted by CARS-SPA most accurately predicted the MC (RPD = 6.4167, RP2 = 0.9757, RMSEP = 0.0442 g·100 g−1) and AC (RPD = 13.0308, RP2 = 0.9941, RMSEP = 0.0216 mmol·10 g−1). The results showed that the XGBoost model could more accurately predict the MC and AC of the fermented grains from hyperspectral images of the grains, providing an effective method for the rapid analysis of raw materials used in the fermentation of liquor. [ABSTRACT FROM AUTHOR]

Starch and alcohol serve as pivotal indicators in assessing the quality of lees fermentation. In this paper, two hyperspectral imaging (HSI) techniques (visible–near‐infrared (Vis–NIR) and NIR) were utilized to acquire separate HSI data, which were then fused and analyzed toforecast the starch and alcohol contents during the fermentation of lees. Five preprocessing methods were first used to preprocess the Vis–NIR, NIR, and the fused Vis–NIR and NIR data, after which partial least squares regression models were established to determine the best preprocessing method. Following, competitive adaptive reweighted sampling, successive projection algorithm, and principal component analysis algorithms were used to extract the characteristic wavelengths to accurately predict the starch and alcohol levels. Finally, support vector machine (SVM)‐AdaBoost and XGBoost models were built based on the low‐level fusion (LLF) and intermediate‐level fusion (ILF) of single Vis–NIR and NIR as well as the fused data. The results showed that the SVM‐AdaBoost model built using the LLF data afterpreprocessing by standard normalized variable was most accurate for predicting the starch content, with an RP2$\ R_P^2$ of 0.9976 and a root mean square error of prediction (RMSEP) of 0.0992. The XGBoost model built using ILF data was most accurate for predicting the alcohol content, with an RP2$R_P^2$ of 0.9969 and an RMSEP of 0.0605. In conclusion, the analysis of fused data from distinct HSI technologies facilitates rapid and precise determination of the starch and alcohol contents in fermented grains. [ABSTRACT FROM AUTHOR]

BACKGROUND: Wheat is one of the key ingredients used to make Chinese liquor, and its saccharification power and protein content directly affect the quality of the liquor. In pursuit of a non‐destructive assessment of wheat components and the optimization of raw material proportions in liquor, this study introduces a precise predictive model that integrates hyperspectral imaging (HSI) with stacked ensemble learning (SEL). RESULTS: This study extracted hyperspectral information from 14 different varieties of wheat and employed various algorithms for preprocessing. It was observed that multiplicative scatter correction (MSC) emerged as the most effective spectral preprocessing method. The feature wavelengths were extracted from the preprocessed spectral data using three different feature extraction methods. Then, single models (support vector machine (SVM), backpropagation neural network (BPNN), random forest (RF), and gradient boosting tree (XGBoost)) and a SEL model were developed to compare the prediction accuracies of the SEL model and the single models based on the full‐band spectral data and the characteristic wavelengths. The findings indicate that the MSC–competitive adaptive reweighted sampling–SEL model demonstrated the highest prediction accuracy, with Rp2 (test set‐determined coefficient) values of 0.9308 and 0.9939 for predicting the saccharification power and protein content and root mean square error of the test set values of 0.0081 U and 0.0116 g kg−1, respectively. CONCLUSION: The predictive model established in this study, integrating HSI and SEL models, accurately detected wheat saccharification power and protein content. This validation underscores the practical potential of the SEL model and holds significant importance for non‐destructive component analysis of raw materials used in liquor. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Flower colour, as an important morphological marker, plays an essential role in improving the identification efficiency of the purity seed in hybrid production. However, the molecular mechanism of white‐flower trait has not been reported in pumpkin (Cucurbita moschata D.). In this study, we obtained a white‐flower mutant (wf) through the ethyl methane sulfonate (EMS) mutagenesis of inbred line N87 (yellow flower). F2 populations were then constructed by crossing wf mutant and N87 plant to fine map the genes controlling white‐flower trait in pumpkin. Phenotypic identification revealed that carotenoid content significantly decreased in the petals of wf mutants compared with N87 plants. Genetic analysis indicated that the white flower mutant trait was controlled by a single recessive gene, Cmowf. Using bulked segregant analysis and KASP phenotyping, Cmowf was mapped to a 762 kb region on chromosome 14 containing three annotated genes. Among them, a nonsynonymous single‐nucleotide polymorphisms mutation was identified only in CmoCh14G005820 gene, which encoded a DUF1997 family protein. Compared with CmoDUF1997 amino acid sequences between the wf mutants and N87 plants, the critical amino acid mutations (early termination of amino acids) occurred in wf mutants, so CmoCh14G005820 was predicted as a potential candidate for controlling the white‐flower trait. RNA‐sequencing analysis revealed that the expression of CmoCh14G005820 and most genes involved in carotenoid biosynthesis was significantly downregulated in wf mutants, whereas the expression of several genes responsible for carotenoid degradation was upregulated in wf mutants. This finding suggested that carotenoid metabolism may participate in the formation of flower colour in pumpkin. Overall, our results provided a theoretical basis for understanding the genetic mechanisms underlying white‐flower formation in pumpkin. [ABSTRACT FROM AUTHOR]

Scorodite (FeAsO4·2H2O) is an ideal material for the fixation of arsenic that has attracted considerable research interest in recent decades. However, the position of the H atom in the scorodite crystal structure, water molecular configuration, surface morphology, and chemical state of the surface atoms have not been reported. In this work, density functional theory (DFT) is used to optimize the scorodite crystal structure, and the atomic bonding is analyzed. At the same time, a surface model is constructed to calculate the configuration and electronic structure of the surface atoms for different coordination groups. The results show that the tetrahedral [AsO4] and octahedral [FeO4(2H2O)] groups in the scorodite crystal structure have good stability(geometry configuration), and the covalent bond strength between the As atom and the bridged oxygen atom (Ob) is greater than that between the Ob atom and the Fe atom. The water molecules in the crystal structure do not seriously deform and ionize. The configuration of the water molecules remains stable through electrostatic interactions (Ow–Fe) and hydrogen bonding (H–Ob). The Fe atoms on the surface of scorodite can coordinate with OH and H2O, while the As atoms can only form a stable coordination with OH. When an Fe atom on the surface coordinates with two H2O atoms, the Fe atom will shrink to the inside of the bulk. With the increase in the hydroxylation number of the Fe atom, the bonding strength between the Fe atom and the Ob atom decreases. Different surface configurations do not affect the stability(geometry configuration) of the [AsO4] structure. In addition, the surface water molecular layer has a very weak effect on the surface coordination configuration. By contrast, in the surface configuration of the (W + OH) structure, the change in the surface atomic layer spacing is the smallest. [ABSTRACT FROM AUTHOR]

BACKGROUND: The purity of sorghum varieties is an important indicator of the quality of raw materials used in the distillation of liquors. Different varieties of sorghum may be mixed during the acquisition process, which will affect the flavor and quality of liquor. To facilitate the rapid identification of sorghum varieties, this study proposes a sorghum variety identification model using hyperspectral imaging (HSI) technology combined with convolutional neural network (AlexNet). RESULTS: First, the watershed algorithm, which was modified with the extended‐maxim transform, was used to segment the hyperspectral images of a single sorghum grain. The isolated forest algorithm was used to eliminate abnormal spectral data from the complete spectral data. Secondly, the AlexNet model of sorghum variety identification was established based on the two‐dimensional gray image data of sorghum grain in group 1. The effects of different preprocessing methods and different convolution kernel sizes on the performance of the AlexNet model were discussed. The eigenvalues of the last layer of the AlexNet model were visualized using the t‐distributed random neighborhood embedding method, which is used to evaluate the separability of features extracted by the AlexNet model. The performance differences between the optimal AlexNet model and traditional machine learning models for sorghum variety identification were compared. Finally, the varieties of sorghum grains in groups 2 and 3 were identified based on the optimal AlexNet model, and the average accuracy values of the test set reached 95.62% and 95.91% respectively. CONCLUSION: The results in this study demonstrated that HSI combined with the AlexNet model could provide a feasible technical approach for the detection of sorghum varieties. © 2022 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

The analysis of the surface chemical behavior of pyrite is highly crucial in the fields of environmental conservation, metal extraction, and flotation separation. In this paper, the mechanism of atomic reconstruction on the pyrite surface and the adsorption behavior of O2 on a reconstructed surface are calculated by density functional theory (DFT). Different reconstruction surfaces were constructed by deleting S and Fe atoms on the (100) surface of pyrite. In addition, the geometric configuration, formation energy, binding energy, cohesion energy, and surface electronic properties of the reconstruction surface were calculated. The adsorption energies and geometric configurations of O2 on different reconstructed surfaces were also determined. The results show that under Fe-poor conditions, the charge of Fe atoms increases, and S atoms form Sn on the reconstructed surface. The binding energy between the Sn and the substrate (ideal surface) is lower, which is similar to the Sn adsorption on the substrate surface with the Fe atom as the site. Sn has high cohesive energy and is resistant to being attacked by oxidants, which leads to structural collapse, and a low affinity for O2. Under S-poor conditions, the –[Fe–S]n– plane structure formed on the reconstructed surface. The –[Fe–S]n– structure stably bonds to the substrate by an Fe–S bond, and exhibits strong binding energy. However, the –[Fe–S]n– structure has low cohesive energy and exhibits thermodynamic instability. In contrast, O2 shows a strong affinity for the –[Fe–S]n– structure, indicating that the deficiency of the S atom promotes the surface oxidation reaction. The mechanism of atomic reconstruction on the surface of pyrite is of utmost importance for understanding its surface chemical behavior. [ABSTRACT FROM AUTHOR]