Discriminative feature analysis of dairy products based on machine learning algorithms and Raman spectroscopy.

Discriminant analysis of similar food samples is an important aspect of achieving food quality control. The effective combination of Raman spectroscopy and machine learning algorithms has become an extremely attractive approach to develop intelligent discrimination techniques. Feature spectral analysis can help researchers gain a deeper understanding of the data patterns in food quality discrimination. Herein, this work takes the discrimination of three brands of dairy products as an example to investigate the Raman spectral feature based on the support vector machines (SVM), extreme learning machines (ELM) and convolutional neural network (CNN) algorithms. The results show that there are certain differences in the optimal spectral feature interval corresponding to different machine learning algorithms. Selecting the appropriate spectral feature interval can maintain high recognition accuracy and improve the computational efficiency of the algorithm. For example, the SVM algorithm has a recognition accuracy of 100% in the 890-980 cm-1, 1410-1500 cm-1 fusion spectral range, which takes about 200 s. The ELM algorithm also has a recognition accuracy of 100% in the 890-980 cm-1, 1410-1500 cm-1 fusion spectral range, which takes less than 0.3 s. The CNN algorithm has a recognition accuracy of 100% in the 890-980 cm-1, 1050-1180 cm-1, 1410-1500 cm-1 fusion spectral range, which takes about 80 s. In addition, by analyzing the distribution of spectral feature intervals based on Euclidean distance, the distribution of experimental samples based on feature spectra is visually displayed. Through the spectral feature analysis process of similar samples, a set of analysis strategies is provided to deeply reveal the data foundation of classification algorithms, which can provide reference for the analysis of relevant discriminative research patterns.

Machine learning-assisted MALDI-TOF MS toward rapid classification of milk products.

This study established a method for rapid classification of milk products by combining matrix assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) analysis with machine learning techniques. The analysis of 2 different types of milk products was used as an example. To select key variables as potential markers, integrated machine learning strategies based on 6 feature selection techniques combined with support vector machine (SVM) classifier were implemented to screen the informative features and classify the milk samples. The models were evaluated and compared by accuracy, Akaike information criterion (AIC), and Bayesian information criterion (BIC). The results showed the least absolute shrinkage and selection operator (LASSO) combined with SVM performs best, with prediction accuracy of 100 ± 0%, AIC of -360 ± 22, and BIC of -345 ± 22. Six features were selected by LASSO and identified based on the available protein molecular mass data. These results indicate that MALDI-TOF MS coupled with machine learning technique could be used to search for potential key targets for authentication and quality control of food products.

Historical reconstruction of glacier mass balance and its contribution to water resources in the Sawir Mountains from 2000 to 2020.

Glacial changes are crucial to regional water resources and ecosystems in the Sawir Mountains. However, glacial changes, including the mass balance and glacial meltwater of the Sawir Mountains, have sparsely been reported. Three model calibration strategies were constructed including a regression model based on albedo and in-situ mass balance of Muz Taw Glacier (A-Ms), regression model based on albedo and geodetic mass balance of valley, cirque, and hanging glaciers (A-Mr), and degree-day model (DDM) to obtain a reliable glacier mass balance in the Sawir Mountains and provide the latest understanding in the contribution of glacial meltwater runoff to regional water resources. The results indicated that the glacial albedo reduction was significant from 2000 to 2020 for the entire Sawir Mountains, with a rate of 0.015 (10a)-1, and the spatial pattern was higher in the east compared to the west. Second, the three strategies all indicated that the glacier mass balance has been continuously negative during the past 20 periods, and the average annual glacier mass balance was -1.01 m w.e. Third, the average annual glacial meltwater runoff in the Sawir Mountains from 2000 to 2020 was 22 × 106 m3, and its contribution to streamflow was 25.81 % from 2000 to 2018. The glacier contribution rates in the Ulkun- Ulastu, Lhaster, and Kendall River basins were 31.37 %, 22.51 %, and 19.27 %, respectively.

EEG microstate analysis reveals large-scale brain network alterations in depressed adolescents with suicidal ideation.

BACKGROUND: Accumulating evidence showed abnormalities in brain network connectivity in depressive individuals with suicidal ideation (SI). We aimed to investigate the large-scale brain network dynamics in adolescents with SI and major depressive disorder (MDD). METHODS: We recruited 47 first-episode drug-naïve adolescents with MDD and SI, 26 depressed adolescents without SI (noSI), and 26 age-matched healthy controls (HC). The Columbia Suicidal Ideation Severity Scale (C-SSRS) was utilized to assess suicide ideation. We acquired 64-channel resting-state EEG recordings from all subjects and used microstate analysis to investigate the large-scale brain network dynamics. RESULTS: We observed a significant reduction in the occurrence and coverage of microstate B within the SI group when contrasted with the noSI group. Conversely, there was a significant increase in the occurrence and coverage of microstate A in the SI group as compared to the HC group. Additionally, we observed heightened transition probabilities from microstates D and C to microstate A in the SI group; meanwhile, transitions from microstate D to B were more prevalent in the noSI group. Furthermore, the noSI group exhibited a significant decline in the transition probabilities from microstate D to microstate C. LIMITATIONS: The cross-sectional nature limits the capacity to determine whether microstate dynamics have prognostic significance for SI. CONCLUSION: We provided evidence that depressed adolescents with SI have a distinct pattern in microstate dynamics compared to those without SI. These findings suggest that microstate dynamics might serve as a potential neurobiomarker for identifying SI in depressed adolescents.

Identification of Radix Bupleuri From Different Geographic Origins Using Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry and Support Vector Machine Algorithm.

BACKGROUND: The geographic origin of Radix bupleuri is an important factor affecting its efficacy, which needs to be effectively identified. OBJECTIVE: The goal is to enrich and develop the intelligent recognition technology applicable to the identification of the origin of traditional Chinese medicine. METHOD: This article establishes an identification method of Radix bupleuri geographic origin based on matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) and support vector machine (SVM) algorithm. The Euclidean distance method is used to measure the similarity between Radix bupleuri samples, and the quality control chart method is applied to quantitatively describe their quality fluctuation. RESULTS: It is found that the samples from the same origin are relatively similar and mainly fluctuate within the control limit, but the fluctuation range is large, and it is impossible to distinguish the samples from different origins. The SVM algorithm can effectively eliminate the impact of intensity fluctuations and huge data dimensions by combining the normalization of MALDI-TOF MS data and the dimensionality reduction of principal components, and finally achieve efficient identification of the origin of Radix bupleuri, with an average recognition rate of 98.5%. CONCLUSIONS: This newly established approach for identification of the geographic origin of Radix bupleuri has been realized, and it has the advantages of objectivity and intelligence, which can be used as a reference for other medical and food-related research. HIGHLIGHTS: A new intelligent recognition method of medicinal material origin based on MALDI-TOF MS and SVM has been established.

Corrigendum: Effect of storage time on the silage quality and microbial community of mixed maize and faba bean in the Qinghai-Tibet Plateau.

[This corrects the article DOI: 10.3389/fmicb.2022.1090401.].

Sulfite altered permanganate effects on acetate-enriched short-chain fatty acids production during sludge anaerobic fermentation.

Anaerobic fermentation is a promising method for waste activated sludge (WAS) treatment, but ineffective solubilization and hydrolysis limit its application. The current study examined the function of sodium sulfite (SDS) in potassium permanganate (PP)-conditioned WAS fermentation for short-chain fatty acids (SCFAs) biosynthesis. The presence of SDS in the PP system (PP/SDS) reduced the positive effects of PP on total SCFAs yield (2755 versus 3471 mg COD/L), while effectively increasing the proportion of acetate (from 41 to 81 %). Not only did SDS decrease the promoting effects of PP on WAS solubilization and hydrolysis efficiency by 5-42 %, it also shifted microbial metabolic pathways to favor acetate production. In addition, the amino acid metabolism with acetate as end product was enhanced. Moreover, PP/SDS inhibited methanogenesis, resulting in an accumulation of acetate in high quantities. Thus, the current study a provided insight and direction for effective WAS treatment with acetate-enriched SCFAs production.

Research on the identification and detection of field pests in the complex background based on the rotation detection algorithm.

As a large agricultural and population country, China's annual demand for food is significant. The crop yield will be affected by various natural disasters every year, and one of the most important factors affecting crops is the impact of insect pests. The key to solving the problem is to detect, identify and provide feedback in time at the initial stage of the pest. In this paper, according to the pest picture data obtained through the pest detection lamp in the complex natural background and the marking categories of agricultural experts, the pest data set pest rotation detection (PRD21) in different natural environments is constructed. A comparative study of image recognition is carried out through different target detection algorithms. The final experiment proves that the best algorithm for rotation detection improves mean Average Precision by 18.5% compared to the best algorithm for horizontal detection, reaching 78.5%. Regarding Recall, the best rotation detection algorithm runs 94.7%, which is 7.4% higher than horizontal detection. In terms of detection speed, the rotation detection time of a picture is only 0.163s, and the model size is 66.54MB, which can be embedded in mobile devices for fast detection. This experiment proves that rotation detection has a good effect on pests' detection and recognition rate, which can bring new application value and ideas, provide new methods for plant protection, and improve grain yield.

Enhanced fluoride removal from water by nanosized cerium oxides impregnated porous polystyrene anion exchanger.

Efficient elimination of fluoride from wastewater is an urgent need for ensuring water safety. In the present study, a stable and reusable nanocomposite (NCO@PAE) was synthesized by impregnating nanosized cerium oxides (NCO) inside a porous polystyrene anion exchanger (PAE) host for efficient fluoride removal from wastewater. The newly fabricated NCO@PAE exhibited excellent resistance to acid and alkali environment, allowing it to be utilized in a wide pH range (2-12). Fluoride uptake onto NCO@PAE was a pH-dependent process, which could reach the maximum capacity at pH 3.0. Compared with its host PAE, NCO@PAE showed conspicuous adsorption affinity towards fluoride in the coexistence of other competing anions at high concentrations. Adsorption kinetics confirmed its high efficiency for achieving equilibrium within 120 min. Fixed-bed adsorption runs demonstrated that the effective processing capacity of NCO@PAE for synthetic fluoride-containing wastewater (initial fluoride 2.5 mg/L) was about ~330 BV (bed volume), while only 22 BV for the host PAE. The exhausted NCO@PAE could be effectively revived by a simple in-situ desorption method for long-term cycle operation without conspicuous capacity loss. All the results indicated that NCO@PAE is a reliable and promising adsorbent for water defluoridation.

Effect of storage time on the silage quality and microbial community of mixed maize and faba bean in the Qinghai-Tibet Plateau.

Tibetan Plateau is facing serious shortage of forage in winter and spring season due to its special geographical location. Utilization of forages is useful to alleviate the forage shortage in winter and spring season. Consequently, the current study was aimed to evaluate the influence of storage time on the silage quality and microbial community of the maize (Zea mays L.) and faba bean (Vicia faba L.) mixed silage at Qinghai-Tibet Plateau. Maize and faba bean were ensiled with a fresh weight ratio of 7:3, followed by 30, 60, 90, and 120 days of ensiling. The results showed the pH value of mixed silage was below 4.2 at all fermentation days. The LA (lactic acid) content slightly fluctuated with the extension of fermentation time, with 33.76 g/kg DM at 90 days of ensiling. The AA (acetic acid) and NH3-N/TN (ammonium nitrogen/total nitrogen) contents increased with the extension of fermentation time and no significantly different between 90 and 120 days. The CP (crude protein) and WSC (water soluble carbohydrate) contents of mixed silage decreased significantly (P < 0.05) with ensiling time, but the WSC content remained stable at 90 days. The Proteobacteria was the predominant phyla in fresh maize and faba bean, and Pseudomonas and Sphingomonas were the predominant genera. After ensiling, Lactobacillus was the prevalent genus at all ensiling days. The relative abundance of Lactococcus increased rapidly at 90 days of ensiling until 120 days of fermentation. Overall, the storage time significant influenced the silage fermentation quality, nutrient content, and microbial environment, and it remained stable for 90 days of ensiling at Qinghai-Tibet Plateau. Therefore, the recommended storage time of forage is 90 days in Qinghai-Tibet Plateau and other cool areas.

Bayesian Discriminant Analysis of Yogurt Products Based on Raman Spectroscopy.

BACKGROUND: The quality discrimination of dairy products is an important basis on which to achieve quality assurance. OBJECTIVE: Taking the discriminant analysis of brand yogurt products as an example, a new rapid discriminant method can be constructed. METHOD: The first three principal components were selected as the pattern vectors of the samples. Then, at random, 75% of the samples were collected as a training set, and their mean values and covariance matrices were calculated to construct a Gauss Bayesian discriminant model. The remaining 25% of samples were employed as a test set, and the pattern vectors of each sample were input into the above model. Next, the posterior probability of each sample in relation to each category could be obtained. Results: The category corresponding to the maximum posterior probability as the brand classification of each sample was defined. CONCLUSIONS: We constructed a Gauss Bayesian discriminant model to discriminate these different yogurt products after the principal component feature extraction of Raman properties. The results indicate the rationality and wide application prospects of this approach. HIGHLIGHTS: A fast dairy product discriminant method based on Gauss Bayesian model and Raman spectroscopy was established.

Deterioration of biological pollutants removal induced by linear alkylbenzene sulphonates in sequencing batch reactors: Insight of sludge characteristics, microbial community and metabolic activity.

Linear alkylbenzene sulfonates (LAS) are widely detected in wastewater, and pose potential risks to environment. The influences of LAS on the typical pollutants removal in sequencing batch reactors (SBRs) were evaluated. The results indicated that the removal efficiency of COD, NH4+ and PO43- was respectively reduced by 10.5-27.5%, 5.4-7.3% and11.6-28.9% with the exposure of 10-50 mg/L LAS. Mechanisms investigation found that LAS disrupted the sludge structure and reduced the biomass in reactors due to the saponification effects. Also, the presence of LAS altered the microbial community of activated sludge, and reduced the abundances of functional bacterial responsible for pollutants removal (i.e.Candidatus Accumulibacter, Nitrospira, Denitratisoma and etc.). Moreover, the LAS exhibited negative impacts on the microbial activity with increased LDH release but decreased ATP concentration. The genes expressions for microbial metabolism (i.e. carbohydrate metabolisms, energy metabolism) and typical pollutants removal (i.e. electron transport, phosphonate transport) were all downregulated in LAS-exposed SBRs.

Red Fluorescent Carbon Dot Powder for Accurate Latent Fingerprint Identification using an Artificial Intelligence Program.

Development and comparison of the latent fingerprints (LFPs) are two major studies in detection and identification of LFPs, respectively. However, integrated research studies on both fluorescent materials for LFP development and digital-processing programs for LFP comparison are scarcely seen in the literature. In this work, highly efficient red-emissive carbon dots (R-CDs) are synthesized in one pot and mixed with starch to form R-CDs/starch phosphors. Such phosphors are comparable with various substrates and suitable for the typical powder dusting method to develop LFPs. The fluorescence images of the developed LFPs are handled with an artificial intelligence program. For the optimal sample, this program presents an excellent matching score of 93%, indicating that the developed sample has very high similarity with the standard control. Our results are significantly better than the benchmark obtained by the traditional method, and thus, both the R-CDs/starch phosphors and the digital processing program fit well for the practical applications.

Adaptive compressed sensing of Raman spectroscopic profiling data for discriminative tasks.

Raman spectroscopy is widely used in discriminative tasks. It provides a wide-range physio-chemical fingerprint in a rapid and non-invasive way. The Raman spectrometry uses a sensor array to convert photon signals into digital spectroscopic data. This analog-to-digital process can benefit from the compressed sensing (CS) technique. The major benefits include less memory usage, shorter acquisition time, and more cost-efficient sensor. Traditional compressed sensing and reconstruction is a series of mathematical operations performed on the signal. Meanwhile, for discriminative tasks, both the signal and the categorical information are involved. For such scenarios, this paper proposes a method that uses both domain signal and categorical information to optimize CS hyper-parameters, including 1) the sampling ratio or the sensing matrix, 2) the basis matrix for the sparse transform, and 3) the regularization rate or shrinkage factor for L1-norm minimization. A case study of formula milk brand identification proves the proposed method can generate effective compressed sensing while preserving enough discriminative power in the reconstructed signal. Under the optimized hyper-parameters, a 100% classification accuracy is retained by only sampling 20% of the original signal.

The statistical fusion identification of dairy products based on extracted Raman spectroscopy.

At present, practical and rapid identification techniques for dairy products are still scarce. Taking different brands of pasteurized milk as an example, they are all milky white in appearance, and their Raman spectra are very similar, so it is not feasible to identify them directly using the naked eye. In the current work, a clear feature extraction and fusion strategy based on a combination of Raman spectroscopy and a support vector machine (SVM) algorithm was demonstrated. The results showed a 58% average recognition accuracy rate for dairy products as based on the original Raman full spectral data and up to nearly 70% based on a single spectral interval. Data normalization processing effectively improved the recognition accuracy rate. The average recognition accuracy rate of dairy products reached 91% based on the normalized Raman full spectral data or nearly 85% based on a normalized single spectral interval. The fusion of multispectral feature regions yielded high accuracy and operation efficiency. After screening and optimizing based on SVM algorithm, the best spectral feature intervals were determined to be 335-354 cm-1, 435-454 cm-1, 485-540 cm-1, 820-915 cm-1, 1155-1185 cm-1, 1300-1414 cm-1, and 1415-1520 cm-1 under the experimental conditions, and the average identification accuracy rate here reached 93%. The developed scheme has the advantages of clear feature extraction and fusion, and short identification time, and it provides a technical reference for food quality control.

Raman chemical feature extraction for quality control of dairy products.

As a fast information acquisition technique, Raman spectroscopy can be used to control the quality of dairy products. Feature extraction is a necessary processing step to improve the efficiency of Raman spectral data. Principal component analysis is a traditional method that can effectively extract the features and reduce the dimension of spectral data. However, it is difficult to analyze the chemical information of the extracted feature, thus limiting its practical application. In this work, Raman spectral chemical feature extraction was carried out. The quality control of Dingxin dairy products (a famous dairy brand in China; purchased from Heilongjiang Zhaodong Tianlong Dairy Co. Ltd., Heilongjiang, China) was used as an example. Raman peak intensity, peak area, and peak ratio were extracted as chemical features and further investigated using Euclidean distance and the quality fluctuation control chart. The potential quality discrimination ability of the Raman feature extraction methods was demonstrated. The results showed that the Puzhen dairy products (purchased from Inner Mongolia Yinuo Halal Food Co. Ltd., Inner Mongolia, China) and Xueyuan dairy products (used as a control; purchased from Inner Mongolia Wulanchabu City Jining Xueyuan Dairy Co. Ltd., Inner Mongolia, China) could be distinguished from Dingxin dairy products when the Raman chemical features special peak intensity, peak area, and peak ratio were used, and their discriminatory ability increased in sequence. Hence, it was shown that Raman chemical feature extraction can achieve quality control and discriminant analysis of dairy products and that the spectral information is clear.

Accurate and rapid detection of soil and fertilizer properties based on visible/near-infrared spectroscopy.

Accurate information of soil macronutrient contents and fertilizer macronutrient contents is the precondition of precision fertilization; however, how to detect soil and fertilizer information rapidly, reliably, and inexpensively remains a great challenge. Visible and near-infrared (VIS/NIR) diffuse reflectance spectroscopy proves to be an effective tool for extensive investigation of soil and fertilizer properties. This study first collected many soil and chemical fertilizer samples and performed both spectral scanning and chemical analysis. During the correlation between the collected VIS/NIR spectra and the measured data, different spectral pretreatment, sample selection, and wavelength optimization methods were applied for improving the accuracy and robustness of the prediction models. After appropriate spectral processing and selection of representative samples, both principal component regression and genetic algorithm (GA) can adequately reduce the number of variables and pick out the characteristic variables, which not only enhanced prediction speed but also greatly improved prediction accuracy. In particular, using GA-based models, organic matter content (OMC), total N and pH value in soil and N, P, and K contents in fertilizer can all be accurately predicted.

Enhanced Stretch Formability of AZ31 Magnesium Alloy Thin Sheet by Induced Precompression and Sequent Annealing.

In this study, precompression deformation with a strain level of 5.38% along the transverse direction (TD) at room temperature was conducted on a AZ31 magnesium alloy thin sheet with thickness of 1mm. Then subsequent annealing treatment was carried out at various temperatures (200, 300, 400, and 500 °C) to induce static recrystallization (SRX) and grain growth. The stretch formability was also investigated using the hemispherical test. The results showed that the twinning texture induced by the precompression process was nearly inherited by recrystallized grains after annealing process. Grains grew up and the size increased with the increase of annealing temperature. The largest grain size was obtained when annealing at 400 °C. The mechanical properties including strength and ductility decreased due to the development of coarse grains, however, the stretch formability was enhanced significantly. Indeed, the IE-value increased from 2.83 mm in the as-received Mg alloy sheet to 5.78 mm in the precompressed and 400 °C annealed specimens, leading to an improvement of 104%. This was ascribed to the rotated grain orientation and higher activity of (10⁻12) twins in coarse grains.

[Application of Raman Spectroscopy and Pattern Recognition Methods for Determining the Authenticity and Detecting the Adulteration of Milk Powder].

The authenticity and adulteration of dairy products are attracting broad attention in recent years. There is a need to develop rapid, simple and accurate analytical methods for the detection of authenticity and adulteration of dairy products. To discriminate between milk powder samples, Raman spectra of FIRMUS, Nestlé and Being Mate milk powder were collected. The nearest neighbor algorithm (NN)combined with the characteristic peaks were employed for the design of a model. On the basis of 10 cross validation, the average recognition rate was 99.56%. In order to achieve the analysis of the adulteration of milk powder, FIRMUS milk powder was mixed with Nestlé milk powder according to the mass ratio 0 :1, 1 : 3, 1 : 1, 3 : 1 and 1 : 0 to get five kinds of the adulterated milk powder samples. Then, fat was extracted from the adulterated milk powder samples. Raman spectra of the fat were collected, then two methods were employed for the design of models. One was the nearest neighbor algorithm combined with the characteristic peaks, another was the kernel principal component analysis (KPCA) combined with NN. On the basis of 10 cross validation, the average recognition rate reached 93.33% and 98.89%, the average operation time was 0.085 and 0.104 s. The results of this work showed that the nearest neighbor algorithm combined with the characteristic peaks can be applied for the determination of the authenticity of milk powder while Raman-KPCA-NN model can provide a simple, accurate and rapid method to investigate the adulteration of milk power.

[Study on the Recognition of Liquor Age of Gujing Based on Raman Spectra and Support Vector Regression].

It is an important and difficult research point to recognize the age of Chinese liquor rapidly and exactly in the field of liquor analyzing, which is also of great significance to the healthy development of the liquor industry and protection of the legitimate rights and interests of consumers. Spectroscopy together with the pattern recognition technology is a preferred method of achieving rapid identification of wine quality, in which the Raman Spectroscopy is promising because of its little affection of water and little or free of sample pretreatment. So, in this paper, Raman spectra and support vector regression (SVR) are used to recognize different ages and different storing time of the liquor of the same age. The innovation of this paper is mainly reflected in the following three aspects. First, the application of Raman in the area of liquor analysis is rarely reported till now. Second, the concentration of studying the recognition of wine age, while most studies focus on studying specific components of liquor and studies together with the pattern recognition method focus more on the identification of brands or different types of base wine. The third one is the application of regression analysis framework, which cannot be only used to identify different years of liquor, but also can be used to analyze different storing time, which has theoretical and practical significance to the research and quality control of liquor. Three kinds of experiments are conducted in this paper. Firstly, SVR is used to recognize different ages of 5, 8, 16 and 26 years of the Gujing Liquor; secondly, SVR is also used to classify the storing time of the 8-years liquor; thirdly, certain group of train data is deleted form the train set and put into the test set to simulate the actual situation of liquor age recognition. Results show that the SVR model has good train and predict performance in these experiments, and it has better performance than other non-liner regression method such as the Partial Least Squares Regression (PLS) method, and can also be applied in the practice of liquor analysis.