Short wave near infrared (NIR) spectroscopy operated in a partial or full transmission geometry and a point spectroscopy mode has been increasingly adopted for evaluation of quality of intact fruit, both on-tree and on-packing lines. The evolution in hardware has been paralleled by an evolution in the modelling techniques employed. This review documents the range of spectral pre-treatments and modelling techniques employed for this application. Over the last three decades, there has been a shift from use of multiple linear regression to partial least squares regression. Attention to model robustness across seasons and instruments has driven a shift to machine learning methods such as artificial neural networks and deep learning in recent years, with this shift enabled by the availability of large and diverse training and test sets.

Review: The evolution of chemometrics coupled with near infrared spectroscopy for fruit quality evaluation

A rapid and nondestructive tea classification method is of great significance in today’s research. This study uses fluorescence hyperspectral technology and machine learning to distinguish Oolong tea by analyzing the spectral features of tea in the wavelength ranging from 475 to 1100 nm. The spectral data are preprocessed by multivariate scattering correction (MSC) and standard normal variable (SNV), which can effectively reduce the impact of baseline drift and tilt. Then principal component analysis (PCA) and t-distribution random neighborhood embedding (t-SNE) are adopted for feature dimensionality reduction and visual display. Random Forest-Recursive Feature Elimination (RF-RFE) is used for feature selection. Decision Tree (DT), Random Forest Classification (RFC), K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) are used to establish the classification model. The results show that MSC-RF-RFE-SVM is the best model for the classification of Oolong tea in which the accuracy of the training set and test set is 100% and 98.73%, respectively. It can be concluded that fluorescence hyperspectral technology and machine learning are feasible to classify Oolong tea.

Reliable Identification of Oolong Tea Species: Nondestructive Testing Classification Based on Fluorescence Hyperspectral Technology and Machine Learning

/pdf/online-inspection-of-browning-in-yali-pears-using-visible-1qdeekzy.pdf

Online Inspection of Browning in Yali Pears Using Visible-Near Infrared Spectroscopy and Interpretable Spectrogram-Based CNN Modeling

Development of qualitative or quantitative models is essential to exploit the full potential of near infrared (NIR) spectroscopy. In tandem with one-dimensional convolutional neural network (1D-CNN), a data-driven model is developed using NIR spectroscopy to estimate organic contents. First, the 1D-CNN model is designed to capture the features of the NIR spectra by means of several convolutional and pooling operations. Then, the suitable hyper-parameters of 1D-CNN are obtained by using the grid search algorithm to achieve the optimal performance. Furthermore, the dropout operation is added into the 1D-CNN to suppress the overfitting problem by means of removing some neurons, and the probability distribution of throwing follows the Bernoulli distribution. The developed framework is validated by the application in the sugar content estimation of Huangshan Maofeng tea. The experimental results demonstrate that the key features of the NIR spectra are successfully extracted by the proposed strategy; thereby, a new effective scheme for analyzing NIR spectra is provided for food analysis.

Development of a calibration model for near infrared spectroscopy using a convolutional neural network

Magnesium (Mg) plays important roles in photosynthesis, sucrose partitioning, and biomass allocation in plants. However, the specific mechanisms of tea plant response to Mg deficiency remain unclear. In this study, we investigated the effects of Mg deficiency on the quality constituents of tea leaves. Our results showed that the short-term (7 days) Mg deficiency partially elevated the concentrations of polyphenols, free amino acids, and caffeine but decreased the contents of chlorophyll and Mg. However, long-term (30 days) Mg-deficient tea displayed decreased contents of these constituents. Particularly, Mg deficiency increased the index of catechins' bitter taste and the ratio of total polyphenols to total free amino acids. Moreover, the transcription of key genes involved in the biosynthesis of flavonoid, caffeine, and theanine was differentially affected by Mg deficiency. Additionally, short-term Mg deficiency induced global transcriptome change in tea leaves, in which a total of 2522 differentially expressed genes were identified involved in secondary metabolism, amino acid metabolism, and chlorophyll metabolism. These results may help to elucidate why short-term Mg deficiency partially improves the quality constituents of tea, while long-term Mg-deficient tea may taste more bitter, more astringent, and less umami.

Exploring the Effects of Magnesium Deficiency on the Quality Constituents of Hydroponic-Cultivated Tea (Camellia sinensis L.) Leaves.

Although spectral techniques have been widely used as noninvasive analytical tools in various applications, spectral disturbances impair their performance in quantitative analyses. A new approach for effective signal extraction in spectroscopy is proposed to suppress spectral variations that are not related to the component of interest. First, the collected spectral data were pre-treated to eliminate the noise. The least absolute shrinkage and selection operator (LASSO) was then introduced to select wavelengths that were truly essential to the model, in which the shrinkage parameter was determined using a cross-validation method. The net analyte signal (NAS) was extracted from the selected wavelengths to eliminate the spectrum related to the interfering components using the rank annihilation method. A partial least squares regression (PLSR) model was constructed using the extracted NAS. The protein estimate of corn demonstrated that the extracted effective signal incorporated with PLSR outperforms existing methods with acceptable computational complexity. The results show that the proposed approach achieves the extraction of effective spectroscopic signals.

Feature Extraction From Spectroscopy Using LASSO and Net Analyte Signal

Virtual metrology (VM) is crucial for improving process capability and production yield during semiconductor manufacturing processes. However, the performance of VM deteriorates owing to the variable operating regime and the nonlinear characteristics of the process. Herein, Variational inference Gaussian mixture model (VIGMM) and extreme learning machine (ELM) are combined to solve these issues. First, variational inference is conducted on a Gaussian mixture model to determine the number of Gaussian components automatically and the corresponding operating regimes are identified. Subsequently, an extreme learning machine is developed for each operating regime to investigate the nonlinear relationship between process inputs and outputs. Finally, VM is implemented using the corresponding local ELM, which is determined based on the responsibility of the Gaussian components. The feasibility and effectiveness of the proposed methods are validated based on a numerical case and the plasma sputtering process for fabricating thin-film transistor liquid-crystal displays. The proposed VIGMM-ELM can serve as a VM algorithm for manufacturing processes with multiple stages.

Virtual Metrology for Multistage Processes Using Variational Inference Gaussian Mixture Model and Extreme Learning Machine

Estimation of tea quality grade using statistical identification of key variables

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Menghu Li

Author Tools

Chat about Author

Papers

Development of a calibration model for near infrared spectroscopy using a convolutional neural network

Feature Extraction From Spectroscopy Using LASSO and Net Analyte Signal

Virtual Metrology for Multistage Processes Using Variational Inference Gaussian Mixture Model and Extreme Learning Machine

Estimation of tea quality grade using statistical identification of key variables