Feed-forward neural network assisted by discriminant analysis for the spectroscopic discriminantion of cracked spores Ganoderma lucidum: A prospective biotechnology production tool

A major problem for manufacturers of cracked spores Ganoderma lucidum, a traditional functional food/Chinese medicine (TCM), is to ensure that raw materials are consistent as received from the producer. To address this, a feed-forward artificial neural network (ANN) method assisted by linear discriminant analysis (LDA) and principal component analysis (PCA) was developed for the spectroscopic discrimination of cracked spores of Ganoderma lucidum from uncracked spores. 120 samples comprising cracked spores, uncracked spores and concentrate of Ganoderma lucidum were analyzed. Differences in the absorption spectra located at ν1 (1143 - 1037 cm(-1)), ν2 (1660 - 1560 cm(-1)), ν3 (1745 - 1716 cm(-1)) and ν4 (2845 - 2798 cm(-1)) were identified by applying fourier transform infra-red (FTIR) spectroscopy and used as variables for discriminant analysis. The utilization of spectra frequencies offered maximum chemical information provided by the absorption spectra. Uncracked spores gave rise to characteristic spectrum that permitted discrimination from its cracked physical state. Parallel application of variables derived from unsupervised LDA/PCA provided useful (feed-forward) information to achieve 100% classification integrity objective in ANN. 100% model validation was obtained by utilizing 30 independent samples. ν1 was used to construct the matrix-matched calibration curve (n = 10) based on 4 levels of concentration (20%, 40%, 60% and 80% uncracked spores in cracked spores). A coefficient of correlation (r) of 0.97 was obtained. Relative standard deviation (RSD) of 11% was achieved using 100% uncracked spores (n = 30). These results demonstrate the feasibility of utilizing a combination of spectroscopy and prospective statistical tools to perform non destructive food quality assessment in a high throughput environment.