Dataset Information

Classification of multicategory edible fungi based on the infrared spectra of caps and stalks.

ABSTRACT: As a characteristic edible fungus with a high nutritional value and medicinal effect, the Bachu mushroom has a broad market. To distinguish among Bachu mushrooms with high value and other fungi effectively and accurately, as well as to explore a universal identification method, this study proposed a method to identify Bachu mushrooms by Fourier Transform Infrared Spectroscopy (FT-IR) combined with machine learning. In this experiment, two kinds of common edible mushrooms, Lentinus edodes and club fungi, were selected and classified with Bachu mushrooms. Due to the different distribution of nutrients in the caps and stalks, the caps and stalks were studied in this experiment. By comparing the average normalized infrared spectra of the caps and stalks of the three types of fungi, we found differences in their infrared spectra, indicating that the latter can be used to classify and identify the three types of fungi. We also used machine learning to process the spectral data. The overall steps of data processing are as follows: use partial least squares (PLS) to extract spectral features, select the appropriate characteristic number, use different classification algorithms for classification, and finally determine the best algorithm according to the classification results. Among them, the basis of selecting the characteristic number was the cumulative variance interpretation rate. To improve the reliability of the experimental results, this study also used the classification results to verify the feasibility. The classification algorithms used in this study were the support vector machine (SVM), backpropagation neural network (BPNN) and k-nearest neighbors (KNN) algorithm. The results showed that the three algorithms achieved good results in the multivariate classification of the caps and stalks data. Moreover, the cumulative variance explanation rate could be used to select the characteristic number. Finally, by comparing the classification results of the three algorithms, the classification effect of KNN was found to be the best. Additionally, the classification results were as follows: according to the caps data classification, the accuracy was 99.06%; according to the stalks data classification, the accuracy was 99.82%. This study showed that infrared spectroscopy combined with a machine learning algorithm has the potential to be applied to identify Bachu mushrooms and the cumulative variance explanation rate can be used to select the characteristic number. This method can also be used to identify other types of edible fungi and has a broad application prospect.

SUBMITTER: Gao R

PROVIDER: S-EPMC7444812 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Classification of multicategory edible fungi based on the infrared spectra of caps and stalks.

Gao Rui R Chen Cheng C Wang Hang H Chen Chen C Yan Ziwei Z Han Huijie H Chen Fangfang F Wu Yan Y Wang Zhiao Z Zhou Yuxiu Y Si Rumeng R Lv Xiaoyi X

PloS one 20200824 8

As a characteristic edible fungus with a high nutritional value and medicinal effect, the Bachu mushroom has a broad market. To distinguish among Bachu mushrooms with high value and other fungi effectively and accurately, as well as to explore a universal identification method, this study proposed a method to identify Bachu mushrooms by Fourier Transform Infrared Spectroscopy (FT-IR) combined with machine learning. In this experiment, two kinds of common edible mushrooms, Lentinus edodes and clu ...[more]

PMID: 32833991

Similar Datasets

Project description:The two most generally diagnosed Neurodegenerative diseases are the Alzheimer and Parkinson diseases. So this paper presents a fully automated early screening system based on the Capsule network for the classification of these two Neurodegenerative diseases. In this study, we hypothesized that the Neurodegenerative diseases-Caps system based on the Capsule network architecture accurately performs the multiclass i.e. three class classification into either the Alzheimer class or Parkinson class or Healthy control and delivers better results in comparison other deep transfer learning models. The real motivation behind choosing the capsule network architecture is its more resilient nature towards the affine transformations as well as rotational & translational invariance, which commonly persists in the medical image datasets. Apart from this, the capsule networks overcomes the pooling layers related deficiencies from which conventional CNNs are mostly affected and unable to delivers accurate results especially in the tasks related to image classification. The various Computer aided systems based on machine learning for the classification of brain tumors and other types of cancers are already available. Whereas for the classification of Neurodegenerative diseases, the amount of research done is very limited and the number of persons suffering from this type of diseases are increasing especially in developing countries like India, China etc. So there is a need to develop an early screening system for the correct multiclass classification into Alzheimer's, Parkinson's and Normal or Healthy control cases. The Alzheimer disease and Parkinson progression (ADPP) dataset is used in this research study for the training of the proposed Neurodegenerative diseases-Caps system. This ADPP dataset is developed with the aid of both the Parkinson's Progression Markers Initiative (PPMI) and Alzheimer's disease Neuroimaging Initiative (ADNI) databases. There is no such early screening system exist yet, which can perform the accurate classification of these two Neurodegenerative diseases. For the sake of genuine comparison, other popular deep transfer learning models like VGG19, VGG16, ResNet50 and InceptionV3 are implemented and also trained over the same ADPP dataset. The proposed Neurodegenerative diseases-Caps system deliver accuracies of 97.81, 98, 96.81% for the Alzheimer, Parkinson and Healthy control or Normal cases with 70/30 (training/validation split) and performs way better as compare to the other popular Deep transfer learning models.Supplementary informationThe online version contains supplementary material available at 10.1007/s11571-022-09787-1.

Project description:The content of trace impurities, such as interstitial oxygen and substitutional carbon, in silicon is crucial in determining the mechanical and physical characteristics of silicon wafers. The traditional infrared (IR) method is adopted as a normal means to analyse their concentration at home and abroad, but there are two problems. The first problem is the poor representativeness of a single local sampling point because the impurity distribution in a solid sample is not as uniform as that in a liquid sample. The second problem is that interference fringes appear in the infrared spectra of the sample due to the thin wafer (≤ 300 μm thick). Based on this, controversial issues existed regarding the measured trace impurity concentrations between wafer manufacturers and solar cell assembly businessmen who used silicon sheets made by the former. Therefore, multiple transmission-reflection (MTR) infrared (IR) spectroscopy was proposed to solve the problems mentioned above. In the MTR setup, because light passes through different parts of the silicon chip several times, multiple sampling points make the final result more representative. Moreover, the optical path is lengthened, and the corresponding absorbance is enhanced. In addition to amplification of weak signals, the MTR-IR method can eliminate interference fringes via the integrating sphere effect of its special configuration. The signal-to-noise ratio of the corresponding spectrum is considerably improved due to the aforementioned dual effects. Thus, the accuracy and sensitivity of the detection method for trace impurities in silicon chips are greatly increased. In this study, silicon wafers were placed in the MTR setup, and then, their relative properties at room temperature were investigated. The corresponding theoretical calculation and simulation of infrared spectra of silicon chips were provided. This affords an optional method for the semiconductor material industry to analyse trace impurities in their chips.

Dataset Information

Classification of multicategory edible fungi based on the infrared spectra of caps and stalks.

Publications

Classification of multicategory edible fungi based on the infrared spectra of caps and stalks.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets