Development of Noninvasive Classification Methods for Different Roasting Degrees of Coffee Beans Using Hyperspectral Imaging

• Published in: Sensors (Basel, Switzerland) Switzerland), 2018-04, Vol.18 (4), p.1259
• Main Authors: Chu, Bingquan, Yu, Keqiang, Zhao, Yanru, He, Yong
• Format: Article
• Language: English
• Subjects: Caffeine; chemometric methods; Chlorogenic acid; Classification; Coffee; coffee bean; Coffee industry; Food Handling; Hot Temperature; Hyperspectral imaging; Least-Squares Analysis; Principal components analysis; qualitative properties; Reflectance; Roasting; roasting degree; Spectra; Spectral reflectance; Support Vector Machine; Support vector machines; Weight analysis
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s18041259

This study aimed to develop an approach for quickly and noninvasively differentiating the roasting degrees of coffee beans using hyperspectral imaging (HSI). The qualitative properties of seven roasting degrees of coffee beans (unroasted, light, moderately light, light medium, medium, moderately dark, and dark) were assayed, including moisture, crude fat, trigonelline, chlorogenic acid, and caffeine contents. These properties were influenced greatly by the respective roasting degree. Their hyperspectral images (874⁻1734 nm) were collected using a hyperspectral reflectance imaging system. The spectra of the regions of interest were manually extracted from the HSI images. Then, principal components analysis was employed to compress the spectral data and select the optimal wavelengths based on loading weight analysis. Meanwhile, the random frog (RF) methodology and the successive projections algorithm were also adopted to pick effective wavelengths from the spectral data. Finally, least squares support vector machine (LS-SVM) was utilized to establish discriminative models using spectral reflectance and corresponding labeled classes for each degree of roast sample. The results showed that the LS-SVM model, established by the RF selecting method, with eight wavelengths performed very well, achieving an overall classification accuracy of 90.30%. In conclusion, HSI was illustrated as a potential technique for noninvasively classifying the roasting degrees of coffee beans and might have an important application for the development of nondestructive, real-time, and portable sensors to monitor the roasting process of coffee beans.