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Related Concept Videos

Force Classification01:22

Force Classification

Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
IR Frequency Region: Fingerprint Region01:03

IR Frequency Region: Fingerprint Region

IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the C=O, C=N, and C=C occur between 1600–1850 cm−1.
The...
Aggregates Classification01:29

Aggregates Classification

Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
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Classification of Systems-I01:26

Classification of Systems-I

Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
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Applications of IR Spectroscopy: Overview01:11

Applications of IR Spectroscopy: Overview

The non-destructive nature and ability to provide valuable chemical information make IR spectroscopy a versatile technique with broad applications in various scientific and industrial fields. IR spectroscopy is commonly used to identify and characterize organic and inorganic compounds. It provides information about the functional groups present in a molecule and the bonding between atoms. This helps in the structural elucidation of compounds during organic synthesis, pharmaceutical research,...
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Classification of Signals

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Related Experiment Video

Updated: Jun 27, 2026

Construction of Models for Nondestructive Prediction of Ingredient Contents in Blueberries by Near-infrared Spectroscopy Based on HPLC Measurements
10:25

Construction of Models for Nondestructive Prediction of Ingredient Contents in Blueberries by Near-infrared Spectroscopy Based on HPLC Measurements

Published on: June 28, 2016

Apple Origin Classification and Sugar Content Prediction of 'Fuji' Apples Using Near-Infrared Spectroscopy and Deep

Zhanglei Yan1,2,3, Zhiyang Li1,2,3, Zhihui Tang1,2,3

  • 1Key Laboratory of Tarim Oasis Agriculture, Ministry of Education, College of Information Engineering, Tarim University, Alar 843300, China.

Foods (Basel, Switzerland)
|June 26, 2026
PubMed
Summary
This summary is machine-generated.

This study uses near-infrared spectroscopy and deep learning to accurately identify Fuji apple origin and predict soluble solid content (SSC). The Transformer model achieved 96.22% accuracy for origin classification.

Keywords:
Fuji appledeep learningnear-infrared spectroscopyorigin classificationsoluble solid content°Brix prediction

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Area of Science:

  • Agricultural Science
  • Spectroscopy
  • Artificial Intelligence

Background:

  • Accurate apple origin identification and internal quality assessment are crucial for traceability and management.
  • Existing research primarily focuses on origin classification, leaving a gap in integrated approaches.

Purpose of the Study:

  • To develop a dual-task near-infrared spectroscopy (NIRS) framework for simultaneous geographical origin classification and soluble solid content (SSC) prediction in Fuji apples.
  • To evaluate the performance of deep learning models for these tasks.

Main Methods:

  • Collected NIRS data from 375 Fuji apples from three Chinese regions.
  • Applied six deep learning models for origin classification using full-spectrum input.
  • Utilized standard normal variate and Savitzky-Golay filtering for SSC prediction with a DNN model.

Main Results:

  • The Transformer model achieved 96.22% accuracy for Fuji apple origin classification.
  • The DNN model demonstrated strong performance for SSC prediction (MAE=0.5958 °Brix, RMSE=0.7333 °Brix, R²=0.8646).

Conclusions:

  • NIRS combined with deep learning effectively supports both Fuji apple origin authentication and non-destructive SSC assessment.
  • This integrated approach enhances fruit traceability, grading, and post-harvest management.