Raft, Y.Z.; writing–Appl. Sci. 2021, 11,11 ofInstitutional Assessment Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: Information sharing isn’t applicable to this article. Conflicts of Interest: The authors declare no conflict of interest.
applied sciencesArticleEvaluation of Mushrooms Depending on 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 Isethionic acid Endogenous Metabolite 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 happen to be recognized as a extremely nutritional meals for any long time, thanks to their distinct flavor and texture, too as their therapeutic effects. This study proposes a new, straightforward strategy determined by FT-IR evaluation, followed by statistical strategies, to be able to differentiate three wild mushroom species from Romanian spontaneous flora, namely, Armillaria mellea, Boletus edulis, and Cantharellus cibarius. The preliminary data treatment consisted of data set reduction with principal component analysis (PCA), which Buformin Epigenetic Reader Domain supplied scores for the next methods. Linear discriminant analysis (LDA) managed to classify 100 from the three species, along with the cross-validation step from the system returned 97.four of properly classified samples. Only one A. mellea sample overlapped around the B. edulis group. When kNN was employed in the identical manner as LDA, the general percent of correctly classified samples from the training step was 86.21 , even though for the holdout set, the % rose to 94.74 . The lower values obtained for the instruction set have been on account of one particular C. cibarius sample, two B. edulis, and five A. mellea, which had been placed to other species. In any case, for the holdout sample set, only 1 sample from B. edulis was misclassified. The fuzzy c-means clustering (FCM) analysis successfully classified the investigated mushroom samples in accordance with their species, meaning that, in just about every partition, the predominant species had the largest DOMs, when samples belonging to other species had decrease DOMs. Keyword 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 happen to be recognized as a extremely nutritional meals for any long time, due to their specific flavor and texture, too as their therapeutic effects. In the nutritional point of view, mushrooms represent a crucial supply of proteins, fibers, minerals, and polyunsaturated fatty acids, with large variations in their proportions among different species. Relating to vitamin content material, it represents the only vegetarian supply of vitamin D [1] too as a crucial source of B group vitamins [2]. Mor.
