Monitoring of fermentation process parameters of Chinese rice wine using attenuated total reflectance mid-infrared spectroscopy

Effect of selective fermentation on nutritional parameters and techno-functional characteristics of fermented millet-based probiotic dairy product

The primary goal of this study was to assess the effect of selective fermentation on the nutritional and techno-functional characteristics of fermented millet-skim milk-based product. The product was made with HHB-311 biofortified pearl millet (PM) flour, skim milk powder, and isolated cultures (either alone or in combination) of Limosilactobacillus fermentum MS005 (LF) and Lactobacillus rhamnosus GG 347 (LGG). To optimize fermentation time, time intervals 8, 16, and 24 h were explored, while the temperature was kept 37 °C. Results of protein digestibility showed that LF (16 h) and LGG (24 h) fermented samples had significantly higher (P < 0.05) protein digestibility of 90.75 ± 1.6% and 93.76 ± 3.4%, respectively, than that of control (62.60 ± 2.6%). Further, 16 h fermentation with LF showed enhanced iron (39%) and zinc (14%) bioavailability. The results suggested that LF with 16 h fermentation is most suitable for making millet-based fermented products with superior techno-functional attributes and micronutrient bioavailability.

Machine Learning Enhanced Optical Spectroscopy for Disease Detection

Optical spectroscopy plays an important role in disease detection. Improving the sensitivity and specificity of spectral detection has great importance in the development of accurate diagnosis. The development of artificial intelligence technology provides a great opportunity to improve the detection accuracy through machine learning methods. In this Perspective, we focus on the combination of machine learning methods with the optical spectroscopy methods widely used for disease detection, including absorbance, fluorescence, scattering, FTIR, terahertz, etc. By comparing the spectral analysis with different machine learning methods, we illustrate that the support vector machine and convolutional neural network are most effective, which have potential to further improve the classification accuracy to distinguish disease subtypes if these machine learning methods are used. This Perspective broadens the scope of optical spectroscopy enhanced by machine learning and will be useful for the development of disease detection.

Effects of Different Pesticides on the Brewing of Wine Investigated by GC-MS-Based Metabolomics

The application of pesticides is critical during the growth of high-quality grape for wine making. However, pesticide residues have significant influence on the wine flavor. In this study, gas chromatography-mass spectrometry (GC-MS) was performed and the obtained datasets were analyzed with multivariate statistical methods to investigate changes in flavor substances in wine during fermentation. The principal component analysis (PCA) score plot showed significant differences in the metabolites of wine treated with various pesticides. In trials using five pesticides (hexaconazole, difenoconazole, flutriafol, tebuconazole, and propiconazole), more than 86 metabolites were changed. Most of these metabolites were natural flavor compounds, like carbohydrates, amino acids, and short-chain fatty acids and their derivatives, which essentially define the appearance, aroma, flavor, and taste of the wine. Moreover, the five pesticides added to grape pulp exhibited different effects on the metabolic pathways, involving mainly alanine, aspartate and glutamate metabolism, butanoate metabolism, arginine, and proline metabolism. The results of this study will provide new insight into the potential impact of pesticide residues on the metabolites and sensory profile of wine during fermentation.

Comparison of the Fermentation Activities and Volatile Flavor Profiles of Chinese Rice Wine Fermented Using an Artificial Starter, a Traditional JIUYAO and a Commercial Starter

In this study, an artificial starter culture was prepared using the core microbial species of JIUYAO to produce Chinese rice wine (CRW). The fermentation activity and flavor profiles of CRW samples fermented with traditional JIUYAO, a commercial starter culture, and our artificial starter culture were compared. The optimal protectant combination for lyophilization of the artificial starter was established as 15.09% skim milk, 4.45% polyethylene glycol, 1.96% sodium glutamate, and 11.81% maltodextrin. A comparative analysis revealed that the ethanol content of the three CRW samples was similar. The total acid content of the CRW sample fermented with the artificial starter (7.10 g/L) was close to that of the sample fermented with JIUYAO (7.35 g/L), but higher than that of the sample fermented with the commercial starter (5.40 g/L). An electronic nose analysis revealed that the olfactory fingerprints of the CRW samples fermented with JIUYAO and the artificial starter resembled each other. For both above mentioned samples, the flavor profiles determined by gas chromatography–mass spectrometry indicated some differences in the variety and content of the aroma compounds, but the key odorants (odor activity values ≥1), such as isoamyl acetate, ethyl acetate, phenyl alcohol, and isoamyl alcohol, were similar.

Comparison of physicochemical properties, amino acids, mineral elements, total phenolic compounds, and antioxidant capacity of Cuban fruit and rice wines

Physicochemical characterization, amino acids contents, minerals composition, total phenolic compounds, and antioxidant capacity of Cuban wines from different raw materials were studied. The wines studied were grape wines, tropical fruit wines, and rice wines. Twenty-one amino acids were identified and quantified, being Asp and Glu detected in all wines. The highest concentration of total amino acid content was found in wines elaborated from Cimarrona grape subjected to maceration with grape skins, while the raisined mixture grape wine presented the lowest values, probably caused by the amino acid degradation during the dehydration process by sun exposure. Minerals quantified were range amount limits of acceptable according to the OIV recommendation. Total phenolic compounds and antioxidant capacity showed the greatest values in wine from roasting rice. No statistical separation could be clearly observed by multivariate principal component analysis; however, 3 wine groups could be defined taking account the scores on the PC1.

The Passive Contact Stability of Blue Sheep Hoof Based on Structure, Mechanical Properties, and Surface Morphology

As the only component that contacts the ground and rock, the hooves of blue sheep may play a crucial role in their excellent climbing abilities. In this study, we used a combination of techniques, including scanning electron microscopy, infrared spectroscopy and nanoindentation, to characterize the surface morphology, structure, material composition, and mechanical properties of blue sheep hoof and investigate the potential contributions of these properties to the establishment of passive contact stability. Straight and curled microscopic lamellar morphology were found on the hoof surfaces. The cross section of the hoof revealed four layers, and each layer had a unique structure. Finite element analysis was employed to verify that the surface morphology and microstructure effectively contributed to the slip resistance and impact cushioning, respectively. Analyses of the energy and infrared spectra showed that the organic and inorganic substances in different regions of the hoof had similar components but different contents of those components. The hoof was mainly composed of keratin. From the outside to the inside, gradients in both the modulus and hardness were observed. These factors help the hoof alleviate high impact strengths and increase contact stability. These findings further our understanding of the unique mechanism of blue sheep hoof and may help in the development of novel biomimetic materials and mechanical components with enhanced friction and contact stability properties.

Combining Fourier Transform Mid-Infrared Spectroscopy with Chemometric Methods to Detect Adulterations in Milk Powder

Adulteration is one of the major concerns among all the quality problems of milk powder. Soybean flour and rice flour are harmless adulterations in the milk powder. In this study, mid-infrared spectroscopy was used to detect the milk powder adulterated with rice flour or soybean flour and simultaneously determine the adulterations content. Partial least squares (PLS), support vector machine (SVM) and extreme learning machine (ELM) were used to establish classification and regression models using full spectra and optimal wavenumbers. ELM models using the optimal wavenumbers selected by principal component analysis (PCA) loadings obtained good results with all the sensitivity and specificity over 90%. Regression models using the full spectra and the optimal wavenumbers selected by successive projections algorithm (SPA) obtained good results, with coefficient of determination (R2) of calibration and prediction all over 0.9 and the predictive residual deviation (RPD) over 3. The classification results of ELM models and the determination results of adulterations content indicated that the mid-infrared spectroscopy was an effective technique to detect the rice flour and soybean flour adulteration in the milk powder. This study would help to apply mid-infrared spectroscopy to the detection of adulterations such as rice flour and soybean flour in real-world conditions.

Prediction of Moisture Content for Congou Black Tea Withering Leaves Using Image Features and Nonlinear Method

Withering is the first step in the processing of congou black tea. With respect to the deficiency of traditional water content detection methods, a machine vision based NDT (Non Destructive Testing) method was established to detect the moisture content of withered leaves. First, according to the time sequences using computer visual system collected visible light images of tea leaf surfaces, and color and texture characteristics are extracted through the spatial changes of colors. Then quantitative prediction models for moisture content detection of withered tea leaves was established through linear PLS (Partial Least Squares) and non-linear SVM (Support Vector Machine). The results showed correlation coefficients higher than 0.8 between the water contents and green component mean value (G), lightness component mean value (L^*) and uniformity (U), which means that the extracted characteristics have great potential to predict the water contents. The performance parameters as correlation coefficient of prediction set (Rp), root-mean-square error of prediction (RMSEP), and relative standard deviation (RPD) of the SVM prediction model are 0.9314, 0.0411 and 1.8004, respectively. The non-linear modeling method can better describe the quantitative analytical relations between the image and water content. With superior generalization and robustness, the method would provide a new train of thought and theoretical basis for the online water content monitoring technology of automated production of black tea.

Improvement of flavor profiles in Chinese rice wine by creating fermenting yeast with superior ethanol tolerance and fermentation activity

Producing alcoholic beverages with novel flavor are desirable for winemakers. We created fermenting yeast with superior ethanol tolerance and fermentation activity to improve the flavor profiles of Chinese rice wine. Strategies of ethanol domestication, ultraviolet mutagenesis (UV) and protoplast fusion were conducted to create yeast hybrids with excellent oenological characteristic. The obtained diploid hybrid F23 showed a cell viability of 6.2% under 25% ethanol, whereas its diploid parental strains could not survive under 20% ethanol. During Chinese rice wine-making, compared to diploid parents, F23 produced 7.07%-12.44% higher yield of ethanol. Flavor analysis indicated that the total content of flavor compounds in F23 wine was 19.99-26.55% higher than that of parent wines. Specifically, F23 exhibited higher capacity in producing 2-phenylethanol, short-chain and long-chain fatty-acid ethyl-ester than diploid parents. Compared to diploid parents, F23 introduced more flavor contributors with odor activity values (OAVs) above one to Chinese rice wine, and those contributors were found with higher OAVs. Based on principal component analysis (PCA), the flavor characteristic of F23 wine was similar to each of parent wine. Additionally, sensory evaluation showed that F23 wine was highly assessed for its intensive levels in fruit-aroma, alcohol-aroma and mouthfeel. Hybrid F23 not only displayed superior flavor production and oenological performance in making Chinese rice wine, but also could act as potential "mixed-like" starter to enrich wine style and differentiation.

Detection of olive oil adulteration with waste cooking oil via Raman spectroscopy combined with iPLS and SiPLS

Olive oil adulteration with waste cooking oil was detected and quantified by combining optical Raman scattering spectroscopy and chemometrics. Spectra of 96 olive oil samples with waste cooking oil (2.5%, 5%, 10%, 20%, 30% and 50%) were collected by the portable Raman spectroscopy system. iPLS and SiPLS quantitative analysis models were established. The results revealed that spectral data after SNV processing are the best for synergy interval partial least square (SiPLS) modeling and forecast. The root mean squared error of calibration (RMSEC) is 0.0503 and the root mean squared error of validation (RMSEV) is 0.0485. The lower limit of application (LLA) of the proposed method is c[WCO]=0.5%. According to linear regression calculation, the theoretical limit of detection (LOD) of the proposed method is about c[WCO]=0.475%. The established model could make effective quantitative analysis on adulteration of waste cooking oil. It provides a quick accurate method for adulteration detection of waste cooking oil in olive oil.

Effect of exogenous metal ions and mechanical stress on rice processed in thermal-solid enzymatic reaction system related to further alcoholic fermentation efficiency

Metal-rich thermal-solid enzymatic processing of rice combined with yeast fermentation was investigated. 8 Metal ions were exogenously supplied at 0.05, 0.5 and 5mmol/100g (MG) rice prior to static high pressure enzymatic cooking (HPEC) and dynamic enzymatic extrusion cooking (EEC). Treated rice and its fermentation efficiency (FE) were characterized by rapid viscosity analyzer (RVA), UV-Vis, FT-IR and atomic absorption spectrophotometer (AAS). The optimum pH range of enzyme in solid system (>4.9) was broader than in a liquid system (>5.5). Cations decreased enzymatic activity in HPEC probably due to metal-induced aggregation of rice matrix with reduced reacting area as well as strengthened structure of starch/polysaccharides modified by metals. While using the EEC with mechanical mixing/shearing, relative activity was activated to 110 and 120% by Mg²⁺ (0.05-0.5MG) and Ca²⁺ (0.05-5MG), respectively. Furthermore, the effectiveness of residual ions to promote further FE was found to follow the order: Ca²⁺>K⁺>Zn²⁺>Mg²⁺>Mn²⁺>Na⁺≈Control>Fe²⁺>Cu²⁺, individually.

Evaluation of the degree of chitosan deacetylation via induced-electrical properties

The properties and functionalities of chitosan are closely related to its degree of deacetylation (DD).

Research progress on the brewing techniques of new-type rice wine

As a traditional alcoholic beverage, Chinese rice wine (CRW) with high nutritional value and unique flavor has been popular in China for thousands of years. Although traditional production methods had been used without change for centuries, numerous technological innovations in the last decades have greatly impacted on the CRW industry. However, reviews related to the technology research progress in this field are relatively few. This article aimed at providing a brief summary of the recent developments in the new brewing technologies for making CRW. Based on the comparison between the conventional methods and the innovative technologies of CRW brewing, three principal aspects were summarized and sorted, including the innovation of raw material pretreatment, the optimization of fermentation and the reform of sterilization technology. Furthermore, by comparing the advantages and disadvantages of these methods, various issues are addressed related to the prospect of the CRW industry.

A online NIR sensor for the pilot-scale extraction process in Fructus aurantii coupled with single and ensemble methods

Model performance of the partial least squares method (PLS) alone and bagging-PLS was investigated in online near-infrared (NIR) sensor monitoring of pilot-scale extraction process in Fructus aurantii. High-performance liquid chromatography (HPLC) was used as a reference method to identify the active pharmaceutical ingredients: naringin, hesperidin and neohesperidin. Several preprocessing methods and synergy interval partial least squares (SiPLS) and moving window partial least squares (MWPLS) variable selection methods were compared. Single quantification models (PLS) and ensemble methods combined with partial least squares (bagging-PLS) were developed for quantitative analysis of naringin, hesperidin and neohesperidin. SiPLS was compared to SiPLS combined with bagging-PLS. Final results showed the root mean square error of prediction (RMSEP) of bagging-PLS to be lower than that of PLS regression alone. For this reason, an ensemble method of online NIR sensor is here proposed as a means of monitoring the pilot-scale extraction process in Fructus aurantii, which may also constitute a suitable strategy for online NIR monitoring of CHM.