AIMC Topic: Least-Squares Analysis

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Predicting oleogels properties using non-invasive spectroscopic techniques and machine learning.

Food research international (Ottawa, Ont.) Feb 26, 2025
Oleogelators are considered food additives that require approval from regulatory authorities. Therefore, classifying these ingredients that may have characteristics (e.g., waxiness), cost and origin (e.g., animal or vegetable) is crucial to ensure co...
Machine Learning Spectroscopy, Near-Infrared Organic Chemicals Spectrum Analysis, Raman Cellulose Principal Component Analysis Food Additives Least-Squares Analysis Soybean Oil Fatty Acids, Nonesterified Waxes Sunflower Oil
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A machine learning approach fusing multisource spectral data for prediction of floral origins and taste components of Apis cerana honey.

Food research international (Ottawa, Ont.) Feb 25, 2025
This study explores the use of near-infrared (NIR), mid-infrared (MIR), and Raman spectral fusion for the rapid prediction of floral origins and main taste components in Apis cerana (A. cerana) honey. Feature-level fusion with the partial least squar...
Sugars Taste Flowers Bees Least-Squares Analysis Amino Acids Animals Honey Spectroscopy, Near-Infrared Machine Learning Spectrum Analysis, Raman
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Raman spectroscopy for colorectal tumor margin assessment: A promising tool for real-time surgical delimitation.

Talanta Feb 21, 2025
Raman spectroscopy is a promising non-invasive technique not only for the rapid and accurate detection of colorectal cancer (CRC) but also for the identification of positive surgical margins. In this study, micro-Raman spectroscopy was used to explor...
Spectrum Analysis, Raman Least-Squares Analysis Humans Discriminant Analysis Colorectal Neoplasms Margins of Excision
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Using visible and NIR hyperspectral imaging and machine learning for nondestructive detection of nutrient contents in sorghum.

Scientific reports Feb 19, 2025
Nondestructive, rapid, and accurate detection of nutritional compositions in sorghum is crucial for agricultural and food industries. In our study, the crude protein, tannin, and crude fat contents of sorghum variety samples were taken as the researc...
Machine Learning Nutrients Nutritive Value Hyperspectral Imaging Algorithms Spectroscopy, Near-Infrared Least-Squares Analysis Tannins Sorghum
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Unveiling the potential of Brachiaria ruziziensis: Comparative analysis of multivariate and machine learning models for biomass and NPK prediction using Vis-NIR-SWIR spectroscopy.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy Feb 18, 2025
This study investigated the development and validation of predictive models for estimating foliar nitrogen (N), phosphorus (P), and potassium (K) contents, along with shoot dry mass (SDM) of Brachiaria ruziziensis L. The approach utilized Vis-NIR-SWI...
Multivariate Analysis Brachiaria Nitrogen Potassium Phosphorus Machine Learning Biomass Spectroscopy, Near-Infrared Support Vector Machine Plant Leaves Least-Squares Analysis
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Exploring the impact of lenticels on the detection of soluble solids content in apples and pears using hyperspectral imaging and one-dimensional convolutional neural networks.

Food research international (Ottawa, Ont.) Feb 7, 2025
In this work, the effect of lenticels on the predictive performance of apple and pear soluble solids content (SSC) models developed based on hyperspectral imaging (HSI) at 380-1010 nm was investigated for the first time. Variations in the spectral pr...
Calibration Malus Pyrus Discriminant Analysis Fruit Hyperspectral Imaging Neural Networks, Computer Algorithms Convolutional Neural Networks Principal Component Analysis Least-Squares Analysis
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Using near-infrared hyperspectral imaging combined with machine learning to predict the components and the origin of Radix Paeoniae Rubra.

Analytical methods : advancing methods and applications Feb 6, 2025
The efficacy and safety of drugs are closely related to the geographical origin and quality of the raw materials. This study focuses on using near-infrared hyperspectral imaging (NIR-HSI) combined with machine learning algorithms to construct content...
Glucosides Hyperspectral Imaging Monoterpenes Principal Component Analysis Paeonia Machine Learning Drugs, Chinese Herbal Algorithms Least-Squares Analysis Spectroscopy, Near-Infrared Support Vector Machine
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A holistic strategy for the in-depth discrimination and authentication of 16 citrus herbs and associated commercial products based on machine learning techniques and non-targeted metabolomics.

Journal of chromatography. A Feb 1, 2025
Citrus-derived raw medicinal materials are frequently used for health care, flavoring, and therapeutic purposes. However, Due to similarities in origin or appearance, citrus herbs are often misused in the market, necessitating effective differentiati...
Metabolomics Discriminant Analysis Citrus Drugs, Chinese Herbal Machine Learning Least-Squares Analysis
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A robust deep learning model for predicting green tea moisture content during fixation using near-infrared spectroscopy: Integration of multi-scale feature fusion and attention mechanisms.

Food research international (Ottawa, Ont.) Jan 30, 2025
Fixation is a critical step in green tea processing, and the moisture content of the leaves after fixation is a key indicator of the fixation quality. Near-infrared spectroscopy (NIRS)-based moisture detection technology is often applied in the tea p...
Least-Squares Analysis Water Temperature Food Handling Plant Leaves Spectroscopy, Near-Infrared Tea Deep Learning
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Cooking loss estimation of semispinalis capitis muscle of pork butt using a deep neural network on hyperspectral data.

Meat science Jan 10, 2025
This study evaluated the performance of a deep-learning-based model that predicted cooking loss in the semispinalis capitis (SC) muscle of pork butts using hyperspectral images captured 24 h postmortem. To overcome low-scale samples, 70 pork butts we...
Hyperspectral Imaging Cooking Paraspinal Muscles Principal Component Analysis Neural Networks, Computer Red Meat Animals Pork Meat Least-Squares Analysis Swine Hydrogen-Ion Concentration Deep Learning
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