Abstract
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.
Highlights
Ganoderma lucidum, a fungus commonly known as Lingzhi, has been used as a traditional functional food/ medicine for centuries by rulers of the Chinese and Japanese dynasties to achieve enhanced vitality and longevity
We developed a workflow involving the direct application of rapid fourier transform infrared (FTIR) and its feed-forward Artificial neural network (ANN) model to perform classification of cracked spores of Ganoderma lucidum originating from a single producer to assess its raw materials consistency
Differences in absorption spectra of Ganoderma lucidum samples The absorption spectra of the homogenized samples were characterized by feature-rich frequency bands representative of major components of Ganoderma lucidum
Summary
A fungus commonly known as Lingzhi, has been used as a traditional functional food/ medicine for centuries by rulers of the Chinese and Japanese dynasties to achieve enhanced vitality and longevity. These formulae take on exotic forms of special tea and mushroom concoction suitable for daily intake as supplements. According to Recital 11 of the European Union Regulation on the hygience of foodstuffs No 852/2004, the application of hazard analysis and critical control point (HACCP) principles to primary produce is not yet generally feasible (Cerf and Donnat, 2011) By this same principle, rapid methods (practicable in a factory environment) are required to test materials prior to its conversion into the finished product. Common to all industrial food processes, the ability to obtain real-time information via an integrated and non destructive quality control system is an attractive option financially, since process delinquencies due to poor materials control may be greatly reduced via an intermediate quality assessment step implemented at the raw materials level
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