Raft, Y.Z.; writing–Appl. Sci. 2021, 11,11 ofInstitutional Overview 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 According to 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 Analysis 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 Based on FT-IR Fingerprint and Chemometrics. Appl. Sci. 2021, 11, 9577. https:// doi.org/10.3390/appAbstract: Edible mushrooms have already been recognized as a hugely nutritional food for any extended time, thanks to their specific flavor and texture, at the same time as their therapeutic effects. This study proposes a new, basic method based on FT-IR analysis, followed by statistical approaches, in order to differentiate three wild mushroom species from Romanian spontaneous flora, namely, Armillaria mellea, Boletus edulis, and Cantharellus cibarius. The preliminary data therapy consisted of information set reduction with principal component evaluation (PCA), which provided Uniconazole manufacturer scores for the subsequent techniques. Linear discriminant evaluation (LDA) managed to classify 100 of the three species, as well as the cross-validation step in the strategy returned 97.four of correctly classified samples. Only 1 A. mellea sample overlapped on the B. edulis group. When kNN was utilised inside the similar manner as LDA, the general percent of correctly classified samples from the instruction step was 86.21 , when for the holdout set, the % rose to 94.74 . The lower values obtained for the training set were due to one C. cibarius sample, two B. edulis, and 5 A. mellea, which have been 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 effectively classified the investigated mushroom samples as outlined by their species, meaning that, in every partition, the predominant species had the biggest DOMs, though samples belonging to other species had reduce DOMs. Keyword phrases: mushrooms; FT-IR; chemometric; machine understanding; fuzzy c-means clusteringAcademic Editor: Alessandra Durazzo Received: 24 September 2021 Accepted: 13 October 2021 Published: 14 October1. Introduction Edible mushrooms happen to be recognized as a highly nutritional meals to get a long time, thanks to their particular flavor and texture, also as their therapeutic effects. From the nutritional point of view, mushrooms represent an important supply of proteins, fibers, minerals, and polyunsaturated fatty acids, with significant variations in their proportions among diverse species. With regards to vitamin content, it represents the only vegetarian supply of vitamin D [1] also as a vital source of B group vitamins [2]. Mor.
