Raman chemical feature extraction for quality control of dairy products

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.

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.

Research Review on Quality Detection of Fresh Tea Leaves Based on Spectral Technology

As the raw material for tea making, the quality of tea leaves directly affects the quality of finished tea. The quality of fresh tea leaves is mainly assessed by manual judgment or physical and chemical testing of the content of internal components. Physical and chemical methods are more mature, and the test results are more accurate and objective, but traditional chemical methods for measuring the biochemical indexes of tea leaves are time-consuming, labor-costly, complicated, and destructive. With the rapid development of imaging and spectroscopic technology, spectroscopic technology as an emerging technology has been widely used in rapid non-destructive testing of the quality and safety of agricultural products. Due to the existence of spectral information with a low signal-to-noise ratio, high information redundancy, and strong autocorrelation, scholars have conducted a series of studies on spectral data preprocessing. The correlation between spectral data and target data is improved by smoothing noise reduction, correction, extraction of feature bands, and so on, to construct a stable, highly accurate estimation or discrimination model with strong generalization ability. There have been more research papers published on spectroscopic techniques to detect the quality of tea fresh leaves. This study summarizes the principles, analytical methods, and applications of Hyperspectral imaging (HSI) in the nondestructive testing of the quality and safety of fresh tea leaves for the purpose of tracking the latest research advances at home and abroad. At the same time, the principles and applications of other spectroscopic techniques including Near-infrared spectroscopy (NIRS), Mid-infrared spectroscopy (MIRS), Raman spectroscopy (RS), and other spectroscopic techniques for non-destructive testing of quality and safety of fresh tea leaves are also briefly introduced. Finally, in terms of technical obstacles and practical applications, the challenges and development trends of spectral analysis technology in the nondestructive assessment of tea leaf quality are examined.

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.

Classification of Industry 4.0 for Total Quality Management: A Review

The philosophy of total quality management is based on meeting quality requirements in all processes and meeting customer needs quickly and accurately through the contribution of all employees. This concept means that all the processes in an enterprise, all the technology used, and all the workforce employed represent the total quality of the enterprise, with the necessary controls and corrections made to ensure that the quality is sustainable. In this study, a detailed literature review and classification study regarding Industry 4.0, Industry 4.0 technologies, and quality has been carried out. The place and importance of quality in Industry 4.0 applications have been revealed by this classification study. In previous studies in the literature, the relationship between Industry 4.0 technologies and quality has not been examined. With this classification study, the importance of quality in Industry 4.0 has emerged, and an analysis has been conducted regarding which quality criteria are used and how often.

Study on extraction methods of polysaccharides from a processed product of Aconitum carmichaeli Debx

Traditional Chinese medicine PaoTianXiong (PTX) is a processed product of Aconitum carmichaeli Debx. with polysaccharide as the main ingredient. The properties of PTX polysaccharide (PTXP) may be affected by different extraction methods. To develop and utilize PTXP better, it is of great significance to study the extraction methods of PTXP. Thus, we extracted PTXPs with dilute alkaline water extraction, ultrasound-assisted extraction, cellulase-assisted extraction, and hot water extraction (HWE), respectively. The characterizations of PTXPs extracted by different methods were analyzed based on purity determination, infrared analysis, molecular weight and monosaccharide composition. And antioxidant experiments of PTXPs were conducted. The results showed that PTXPs extracted by the four extraction methods were all glucan. After purification, the PTXPs showed similar antioxidant activity in vitro. The molecular weight of polysaccharides extracted by the cellulase-assisted method was different from that extracted by the other three methods. Our results showed that not only the yield but also the effect of extraction methods on the properties of PTXP should be considered when selecting the best extraction method. Therefore, HWE was considered to be the best extraction method of PTXP. The yield and purity of purified PTXP were 24.5% and 97.1%, respectively. The optimized extraction conditions were: an extraction temperature of 90 °C, extraction time of 2.17 h, solid-liquid ratio of 1 : 29 (g mL-1), and number of extractions of 2.

Intelligent body behavior feature extraction based on convolution neural network in patients with craniocerebral injury

Patients with craniocerebral injury are in serious condition and inconvenient to take care of. This paper proposes a method of extracting the patient's body behavior feature based on convolution neural network, in order to reduce nursing workload and save hospital costs. The algorithm adopts double network model design, including the patient detection network model and the patient's body behavior feature extraction model. The algorithm is applied to the patient's body behavior detection system, so as to realize the recognition and monitoring of patients and improve the level of intelligent medical care for craniocerebral injury. Finally, the open source framework platform is used to test the patient behavior detection system. The experimental results show that the larger the test data set is, the higher the accuracy of patient body behavior feature extraction is. The average recognition rate of patient body behavior category is 97.8%, which verifies the effectiveness and correctness of the system. The application of convolution neural network connects image recognition with intelligent medical nursing, which provides reference and experience for intelligent medical nursing of patients with craniocerebral injury.

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.

A rapid method for detection adulteration in goat milk by using vibrational spectroscopy in combination with chemometric methods

Because of the second place of milk adulteration in the food fraud lists, the study focused on the investigation of the cow milk as an adulterant in goat milk based on β-carotene presence in cow milk as s rapid method by Raman and Infrared spectroscopy with chemometric techniques.t Partial least squares regression (PLSR) and the soft independent modelling of class Analogy (SIMCA) models have developed to for the prediction of adulteration ratio and β-carotene content of mixtures on the spectral band at around 1373, 1454, and 956 cm-1 for infrared and 1005, 1154, and 1551 cm-1 for Raman spectroscopy respectively. The correlation coefficient for calibration (R2cal), standard error of calibration, standard error of performance, and correlation coefficient for validation (R2val) have calculated for mid-infrared and Raman techniques. The PLSR models showed excellent fit (R2 value > 96) and could accurately determine β-carotene content and percentage of spiked milk in a short time. SIMCA results showed that 20% intervals of the mixture could be differentiated barely from other mixtures by mid-infrared spectroscopy; however, there could not found significant discrimination by Raman spectroscopy. β-carotene could be considered as a biomarker of determination of adulteration concerning β-carotene content and mixture percentage, and discrimination of spiked mixture for the differentiation of goat and cow milk.

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.

At present, practical and rapid identification techniques for dairy products are still scarce.