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AMB Express (2)
Chan, Sheot Harn (2)
Lim, Chee Wei (2)
Tai, Siew Hoon (1)
Visconti, Angelo (1)
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author:("Chan, sheet Harn")
A flow-injection mass spectrometry fingerprinting scaffold for feature selection and quantitation of Cordyceps and Ganoderma extracts in beverage: a predictive artificial neural network modelling strategy
Lim, Chee Wei
Tai, Siew Hoon
Flow-injection mass spectrometry (FI/MS) represents a powerful analytical tool for the quality assessment of herbal formula in dietary supplements. In this study, we described a scaffold (proof-of-concept) adapted from spectroscopy to quantify Cordyceps sinensis and Ganoderma lucidum in a popular Cordyceps sinensis /Ganoderma lucidum -enriched health beverage by utilizing flow-injection/mass spectrometry/artificial neural network (FI/MS/ANN) model fingerprinting method with feature selection capability. Equal proportion of 0.1% formic acid and methanol (v/v) were used to convert extracts of Cordyceps sinensis and Ganoderma lucidum into their respective ions under positive MS polarity condition. No chromatographic separation was performed. The principal m/z values of Cordyceps sinensis and Ganoderma lucidum were identified as: 104.2, 116.2, 120.2, 175.2, 236.3, 248.3, 266.3, 366.6 and 498.6; 439.7, 469.7, 511.7, 551.6, 623.6, 637.7 and 653.6, respectively. ANN models representing Cordyceps sinensis and Ganoderma lucidum were individually trained and validated using three independent sets of matrix-free and matrix-matched calibration curves at concentration levels of 2, 20, 50, 100, 200 and 400 μg mL-1. Five repeat analyses provided a total of 180 spectra for herbal extracts of Cordyceps sinensis and Ganoderma lucidum. Root-mean-square-deviation (RMSE) were highly satisfactory at <4% for both training and validation models. Correlation coefficient (r2) values of between 0.9994 and 0.9997 were reported. Matrix blanks comprised of complex mixture of Lingzhi fermentation solution and collagen. Recovery assessment was performed over two days using six sets of matrix blank (n = 6) spiked at three concentration levels of approximately 83, 166 and 333 mg kg-1. Extraction using acetonitrile provided good overall recovery range of 92-118%. A quantitation limit of 0.2 mg L-1 was reported for both Cordyceps sinensis and Ganoderma lucidum. Intra-day and inter-day RMSE values of 7% or better were achieved. Application of the scaffold in a high-throughput routine environment would imply a significant reduction in effort and time, since the option of having a model driven analytical solution is now available.
Cordyceps sinensis; Ganoderma lucidum; MS; Fingerprinting; ANN; Quantitation
Feed-forward neural network assisted by discriminant analysis for the spectroscopic discriminantion of cracked spores Ganoderma lucidum: A prospective biotechnology production tool
Lim, Chee Wei
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.
Adulteration; Artificial neural network; Cracked spores; Feed-forward; Ganoderma; Spectroscopy
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