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

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.

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.

Spectroscopic technologies and data fusion: Applications for the dairy industry

Increasing consumer awareness, scale of manufacture, and demand to ensure safety, quality and sustainability have accelerated the need for rapid, reliable, and accurate analytical techniques for food products. Spectroscopy, coupled with Artificial Intelligence-enabled sensors and chemometric techniques, has led to the fusion of data sources for dairy analytical applications. This article provides an overview of the current spectroscopic technologies used in the dairy industry, with an introduction to data fusion and the associated methodologies used in spectroscopy-based data fusion. The relevance of data fusion in the dairy industry is considered, focusing on its potential to improve predictions for processing traits by chemometric techniques, such as principal component analysis (PCA), partial least squares regression (PLS), and other machine learning algorithms.