What question did this study set out to answer?

This research aims to develop a non-destructive model for identifying different types of composite Chinese sausages using hyperspectral imaging.

March 28, 2026Human and nation s health0 citations

Hyperspectral Identification of Composite Chinese Sausages

YFYuanxia FuBengbu Medical College

Key Points

This research aims to develop a non-destructive model for identifying different types of composite Chinese sausages using hyperspectral imaging.
Used hyperspectral imaging technology to collect data over 400 to 1000 nm.
Applied three preprocessing algorithms: Multiplicative Scatter Correction, Savitzky-Golay smoothing, and Neighborhood Averaging.
Employed Principal Component Analysis to reduce data dimensionality and enhance modeling efficiency.
Implemented machine learning algorithms, particularly SVM, for classification of sausage types.
Achieved over 99% prediction accuracy with models combining MSC preprocessing and machine learning.
SVM model demonstrated the highest classification accuracy among all tested methods.
The new feature set retained after PCA explained over 95% of data variance.

Abstract

With the increasing diversity of compound sausage types, the development of a rapid, accurate, and non-destructive identification model has become essential. The aim is to develop a robust model that operates without damaging the sausage surface or structure, meeting real-time, lossless, and high-throughput recognition requirements in practical applications. “Lossless recognition” refers to non-destructive analysis of surface spectra using hyperspectral imaging technology – without chemical treatment, grinding, or slicing into the interior of the sausage. Hyperspectral imaging technology was employed to collect spectral data over 400 – 1000 nm for eight types of compound sausages. For each image, 50 sampling regions were randomly selected, and the average reflectance values were extracted. First, three preprocessing algorithms – Multiplicative Scatter Correction (MSC), Savitzky-Golay smoothing (SG), and Neighborhood Averaging (NA) – were applied to the raw hyperspectral data. To enhance modeling efficiency, Principal Component Analysis (PCA) was used to reduce the dimensionality of the original 328 spectral bands, retaining the first 10 principal components, which explained over 95% of the total variance, as the new feature set. The classification results demonstrate that hyperspectral imaging combined with machine learning algorithms can effectively distinguish between the eight compound sausage types. Among all methods, the SVM model exhibited the highest classification accuracy, highlighting its excellent discriminative ability and robustness in high-dimensional hyperspectral data analysis. Classification models that combined MSC preprocessing with any of the three algorithms achieved prediction accuracies of over 99%.Received30.12.2025Accepted1.03.2026

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