Raft, Y.Z.; writing–Appl. Sci. 2021, 11,11 ofInstitutional Evaluation Board Statement: Not applicable. Informed Consent Statement: Not applicable. Information Availability Statement: Data sharing is not applicable to this short article. Conflicts of Interest: The authors declare no conflict of interest.
applied sciencesArticleEvaluation of Mushrooms Determined by FT-IR Fingerprint and ChemometricsIoana Feher 1 , Cornelia Veronica Floare-Avram 1, , Florina-Dorina Covaciu 1 , Olivian Marincas 1 , Romulus Puscas 1 , Dana Alina Magdas 1 and Costel S buNational Institute for Research and Improvement of Isotopic and Molecular Technologies, 67-103 Donat Street, 400293 Cluj-Napoca, Romania; [email protected] (I.F.); [email protected] (F.-D.C.); [email protected] (O.M.); [email protected] (R.P.); [email protected] (D.A.M.) Faculty of Chemistry and Chemical Engineering, Babes-Bolyai University, 11 Arany J os, , 400028 Cluj-Napoca, Romania; [email protected] Correspondence: [email protected]: Feher, I.; Floare-Avram, C.V.; Covaciu, F.-D.; Marincas, O.; Puscas, R.; Magdas, D.A.; S bu, C. Evaluation of Mushrooms Determined by FT-IR Fingerprint and Chemometrics. Appl. Sci. 2021, 11, 9577. https:// doi.org/10.3390/appAbstract: Edible mushrooms have been recognized as a extremely nutritional meals for any lengthy time, due to their precise flavor and texture, also as their therapeutic effects. This study proposes a new, very simple method determined by FT-IR evaluation, followed by statistical approaches, in order to differentiate 3 wild mushroom o-Toluic acid Protocol species from Romanian spontaneous flora, namely, Armillaria mellea, Boletus edulis, and Cantharellus cibarius. The preliminary information remedy consisted of data set reduction with principal component analysis (PCA), which offered scores for the following techniques. Linear discriminant analysis (LDA) managed to classify 100 with the 3 species, as well as the cross-validation step from the approach returned 97.four of appropriately classified samples. Only 1 A. mellea sample overlapped on the B. edulis group. When kNN was made use of in the identical manner as LDA, the general percent of appropriately classified samples from the training step was 86.21 , while for the holdout set, the percent rose to 94.74 . The reduced values obtained for the education set have been on account of one particular C. cibarius sample, two B. edulis, and 5 A. mellea, which were placed to other species. In any case, for the holdout sample set, only one sample from B. edulis was misclassified. The fuzzy c-means clustering (FCM) analysis successfully classified the investigated mushroom samples in accordance with their species, which means that, in every single partition, the predominant species had the greatest DOMs, while samples belonging to other species had lower DOMs. Keywords and phrases: mushrooms; FT-IR; chemometric; machine learning; fuzzy c-means clusteringAcademic Editor: Alessandra Durazzo Received: 24 September 2021 Accepted: 13 October 2021 Published: 14 October1. Introduction Edible mushrooms have been recognized as a extremely nutritional food for any long time, because of their distinct flavor and texture, as well as their therapeutic effects. From the nutritional point of view, mushrooms represent a crucial source of proteins, fibers, minerals, and polyunsaturated fatty acids, with massive variations in their proportions among diverse species. With regards to vitamin content, it represents the only vegetarian supply of vitamin D [1] as well as an important source of B group vitamins [2]. Mor.