Mango DMC and NIR spectra

These datasets contain Near-infrared (NIR) absorbance spectra of the wavelength range 309-149 nm of mango mesocarp with corresponding Dry Matter Content (DMC) values.

The file "MangoDMC_NIR_Data_v3.csv" contains data as used in the publication "Achieving robustness across season, location and cultivar for a NIRS model for intact mango fruit dry matter content" (Postharvest Biology and Technology, 2020, 168:111202; https://www.sciencedirect.com/science/article/pii/S0925521420301629), with addition of data from an additional harvest season, as used in the publication "Evaluation of 1D Convolutional Neural Network in Estimation of Mango Dry Matter Content" (Spectrochimica Acta Part A 2024 311: 124003; https://www.sciencedirect.com/science/article/pii/S1386142524001690). This file is as presented in version 3 of this data repository.

The current version (4) has an additional file ".csv". This file augments the data of version 3 with data from additional instruments and seasons as used in the submitted thesis of Jeremy Walsh, 2024, Central Queensland University, "Deep Learning in Estimation of Fruit Attributes Using Near Infrared Spectroscopy".

本数据集涵盖芒果中果皮的近红外（Near-infrared, NIR）吸收光谱，光谱波长范围为309–149 nm，附带对应的干物质含量（Dry Matter Content, DMC）数值。
文件“MangoDMC_NIR_Data_v3.csv”收录了发表于《采后生物学与技术（Postharvest Biology and Technology）》2020年第168卷、文章编号为111202的论文《用于完整芒果果实干物质含量检测的近红外光谱（Near-Infrared Spectroscopy, NIRS）模型的鲁棒性提升：跨季节、跨地点、跨品种（Achieving robustness across season, location and cultivar for a NIRS model for intact mango fruit dry matter content）》（https://www.sciencedirect.com/science/article/pii/S0925521420301629）中所使用的数据集，并补充了额外收获季的相关数据；该补充数据来自发表于《光谱化学学报A辑（Spectrochimica Acta Part A）》2024年第311卷、文章编号为124003的论文《一维卷积神经网络（1D Convolutional Neural Network）在芒果干物质含量预测中的应用（Evaluation of 1D Convolutional Neural Network in Estimation of Mango Dry Matter Content）》（https://www.sciencedirect.com/science/article/pii/S1386142524001690）。此文件为本数据集仓库的v3版本。
当前版本（v4）新增了一个.csv格式文件，该文件在v3版本数据的基础上进行了扩充，新增数据来源于2024年中央昆士兰大学Jeremy Walsh提交的学位论文《近红外光谱在水果属性预测中的深度学习应用（Deep Learning in Estimation of Fruit Attributes Using Near Infrared Spectroscopy）》中所使用的不同仪器与不同收获季的数据集。

搜集汇总

背景与挑战

背景概述

该数据集包含芒果近红外吸收光谱数据（波长309-149 nm）及对应的干物质含量值，用于研究芒果品质评估。数据基于多个已发表研究，涵盖不同季节、地点和仪器，支持模型开发和验证，如卷积神经网络在干物质含量估计中的应用。