A Spectral Dataset of both Liquid and Solid Milk with Impurities for some Milk Impurity Detection

This dataset was developed for a study focused on the fast, cost-effective, and accurate detection of milk impurities using machine learning algorithms in conjunction with Near-Infrared (NIR) Spectroscopy. Spectral data were gathered using pixel sensors equipped with on-chip filtering technology, incorporating up to 8 wavelength-selective photodiodes within a compact 9x9mm array. The spectrometer divided the spectrum into eight distinct bands, ranging from 400 to 1100 nm, to detect pure or impure milk for various additives in both liquid and solid forms. The additives analyzed include refined sugar, flour, coffee powder, and milk powder. The dataset comprises a total of 20,000 spectral samples.

本数据集专为一项研究开发，该研究旨在结合机器学习算法与近红外（Near-Infrared, NIR）光谱技术，实现牛乳杂质的快速、低成本且精准检测。研究采用搭载片上滤波技术的像素传感器采集光谱数据，该传感器在紧凑的9×9mm阵列中集成了至多8个波长选择性光电二极管。该光谱仪将400至1100nm的光谱范围划分为8个独立波段，用于检测液态或固态下各类添加剂污染的纯净牛乳及掺假牛乳。本次分析涉及的添加剂包括精制糖、面粉、咖啡粉与奶粉。本数据集总计包含20000条光谱样本。