Development of PCA-MLP Model Based on Visible and Shortwave Near Infrared Spectroscopy for Authenticating Arabica Coffee Origins

The Authentication of Gayo Arabica Green Coffee Beans with Different Cherry Processing Methods Using Portable LED-Based Fluorescence Spectroscopy and Chemometrics Analysis

Aceh is an important region for the production of high-quality Gayo arabica coffee in Indonesia. In this area, several coffee cherry processing methods are well implemented including the honey process (HP), wine process (WP), and natural process (NP). The most significant difference between the three coffee cherry processing methods is the fermentation process: HP is a process of pulped coffee bean fermentation, WP is coffee cherry fermentation, and NP is no fermentation. It is well known that the WP green coffee beans are better in quality and are sold at higher prices compared with the HP and NP green coffee beans. In this present study, we evaluated the utilization of fluorescence information to discriminate Gayo arabica green coffee beans from different cherry processing methods using portable fluorescence spectroscopy and chemometrics analysis. A total of 300 samples were used (n = 100 for HP, WP, and NP, respectively). Each sample consisted of three selected non-defective green coffee beans. Fluorescence spectral data from 348.5 nm to 866.5 nm were obtained by exciting the intact green coffee beans using a portable spectrometer equipped with four 365 nm LED lamps. The result showed that the fermented green coffee beans (HP and WP) were closely mapped and mostly clustered on the left side of PC1, with negative scores. The non-fermented (NP) green coffee beans were clustered mostly on the right of PC1 with positive scores. The results of the classification using partial least squares-discriminant analysis (PLS-DA), linear discriminant analysis (LDA), and principal component analysis-linear discriminant analysis (PCA-LDA) are acceptable, with an accuracy of more than 80% reported. The highest accuracy of prediction of 96.67% was obtained by using the PCA-LDA model. Our recent results show the potential application of portable fluorescence spectroscopy using LED lamps to classify and authenticate the Gayo arabica green coffee beans according to their different cherry processing methods. This innovative method is more affordable and could be easy to implement (in terms of both affordability and practicability) in the coffee industry in Indonesia.

Application for Identifying the Origin and Predicting the Physiologically Active Ingredient Contents of Gastrodia elata Blume Using Visible-Near-Infrared Spectroscopy Combined with Machine Learning

Gastrodia elata (G. elata) Blume is widely used as a health product with significant economic, medicinal, and ecological values. Due to variations in the geographical origin, soil pH, and content of organic matter, the levels of physiologically active ingredient contents in G. elata from different origins may vary. Therefore, rapid methods for predicting the geographical origin and the contents of these ingredients are important for the market. This paper proposes a visible-near-infrared (Vis-NIR) spectroscopy technology combined with machine learning. A variety of machine learning models were benchmarked against a one-dimensional convolutional neural network (1D-CNN) in terms of accuracy. In the origin identification models, the 1D-CNN demonstrated excellent performance, with the F1 score being 1.0000, correctly identifying the 11 origins. In the quantitative models, the 1D-CNN outperformed the other three algorithms. For the prediction set of eight physiologically active ingredients, namely, GA, HA, PE, PB, PC, PA, GA + HA, and total, the RMSEP values were 0.2881, 0.0871, 0.3387, 0.2485, 0.0761, 0.7027, 0.3664, and 1.2965, respectively. The Rp2 values were 0.9278, 0.9321, 0.9433, 0.9094, 0.9454, 0.9282, 0.9173, and 0.9323, respectively. This study demonstrated that the 1D-CNN showed highly accurate non-linear descriptive capability. The proposed combinations of Vis-NIR spectroscopy with 1D-CNN models have significant potential in the quality evaluation of G. elata.