Quantitative determination of macro components and classification of some cultivated mushrooms using near‐infrared spectroscopy

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Abstract

Fourier transform near‐infrared spectroscopy (FT‐NIRS) was applied to quantitatively determine the macro components (dry matter, protein, carbohydrate, and usable energy) of different cultivated mushroom species. Prediction models were built by partial least‐square regression (PLSR) using 192 mushroom samples. The validation of the models was based on fivefold cross‐validation and test set validation. The root mean square errors of cross‐validation and prediction (RMSECV/RMSEP) were as follows: dry matter 0.36/0.37% w/w, carbohydrate 1.68/1.76% w/w total carbohydrate 1.50/1.50% w/w, and protein 0.47/0.81% w/w. The goodness of cross‐validation (Q²) was over 0.90 in every case. Interval PLSR was applied for model building in case of energy. The RMSECV was 44 kJ/100 g and the Q² was 0.867. Moreover, six species of mushrooms (Agaricus bisporus, Lentinula edodes, Hericium erinaceus, Pleurotus x hybrid, Hypsizygus tessellatus, Lentinula edodes, Hericium erinaceus) were classified correctly using linear discriminant analysis.

PRACTICAL APPLICATIONS: FT‐NIR technique is an environmentally sound, fast, non‐destructive analytical method that gives professionals great help in quality control during cultivation. The method is ideally suited for checking the content of candidate varieties and, knowing this, for selecting the varieties with the most favorable characteristics. The primary aspect of processing plants and of the processing trade is quality raw materials, because these are the base for quality products. The FT‐NIR method allows obtaining a rapid answer to this issue, and furthermore due to its versatility, also offers the possibility of in‐line, on‐line or at‐line application.