Advances in Feature Selection Methods for Hyperspectral Image Processing in Food Industry Applications: A Review

There is an increased interest in the applications of hyperspectral imaging (HSI) for assessing food quality, safety, and authenticity. HSI provides abundance of spatial and spectral information from foods by combining both spectroscopy and imaging, resulting in hundreds of contiguous wavebands for each spatial position of food samples, also known as the curse of dimensionality. It is desirable to employ feature selection algorithms for decreasing computation burden and increasing predicting accuracy, which are especially relevant in the development of online applications. Recently, a variety of feature selection algorithms have been proposed that can be categorized into three groups based on the searching strategy namely complete search, heuristic search and random search. This review mainly introduced the fundamental of each algorithm, illustrated its applications in hyperspectral data analysis in the food field, and discussed the advantages and disadvantages of these algorithms. It is hoped that this review should provide a guideline for feature selections and data processing in the future development of hyperspectral imaging technique in foods.     

POTATO CRISP MOISTURE ESTIMATION USING NEAR INFRARED SPECTROSCOPY

Near infrared spectral analysis is a widely used tool for non-destructive determination of moisture content in food. Moisture in potato crisps is often measured using near infrared analysis, however shape contours and curvatures in samples causes scattering of light, which reduces the accuracy of the measurement. The piecewise multiple scatter correction algorithm is commonly used to reduce the effect of light scattering from the sample. Implementation of the piecewise multiple scatter correction algorithm requires careful selection of scatter correction window size and combinations of spectral wavelength predictors. This paper considers different search heuristics for the purpose of piecewise multiple scatter correction window size selection and optimal spectral wavelength predictor selection. The search heuristics under consideration are a genetic algorithm, hill climbing, feature selection and full spectrum modeling. Calibration models using partial least square regression are formed from the scatter corrected data. The standard error of a cross-validated calibration data set was used to compare the performance of the different techniques. It has been found that a genetic algorithm produced the lowest cross validated standard error from the techniques considered. This suggests that the use of a genetic algorithm resulted in more judicious selection of near infrared model parameters.     

基于光谱特征的真伪纸币差异分析

科学、严谨的纸币真伪鉴别方法是遏制假钞流通的有效途径和重要手段.随着科技的进步,许多创新鉴别技术不断涌现,其中基于光谱特征的检测技术因准确、高效而被逐渐推广应用.利用光谱反射率特征能有效地区分背景、干扰物与真实目标,其在医疗、军事、食品检测等方面都取得了良好的应用效果.本研究利用真伪纸币的光谱差异性对纸币真伪进行鉴别分析.首先,利用尺度不变特征转换（SIFT）算法提取真、伪纸币的图像特征点,获取描述符幅值较大的特征点,通过测量比较真伪纸币特征点的光谱信息差异,对纸币进行真伪鉴别.     

红外及近红外光谱技术在食品分析检验中的应用进展

红外及近红外波谱技术,是利用物质对红外光区的电磁辐射的选择性吸收来进行结构分析及定性和定量分析,具有快速,方便,样品用量少,不损坏样品等优点。     

Classification of internally damaged almond nuts using hyperspectral imagery

Hyperspectral transmission spectra of almond nuts are studied for discriminating internally damaged almond nuts from normal ones. We introduce a novel internally damaged almond detection method that requires only two sets of ratio features (the ratio of the responses at two different spectral bands) for classification. Our proposed method avoids exhaustively searching the whole feature space by first ordering the set of ratio features and then choosing the best ratio features based on the ordered set. Use of two sets of ratio features for classification is attractive, since it can be used in real-time practical multispectral sensor systems. Experimental results demonstrate that our method gives a higher classification rate than does use of the best feature selection subset of separate wavebands or than does use of feature extraction algorithms using all wavelength data.     

薄层色谱法在保健食品检验应用中的常见问题及解决方法

保健食品中某些作用成分为中药成分，其质量会影响产品质量。薄层色谱法由于具有操作方便、设备简单、显色容易等特点而被广泛应用于保健食品中中药成分分析。薄层色谱法在应用于保健食品检验时，常会遇到薄层板、点样器材、展开缸等器材的选择、供试品溶剂的选择、点样量和展开剂的选择以及操作等方面的问题。本研究对薄层色谱法在保健食品检验应用的常见问题进行汇总，并分析这些问题出现的原因，提出了进一步的解决措施，以期提高薄层色谱法检验结果的准确性。     

食品检验中样品管理的注意事项

食品检验是了解食品质量安全状况、収现风险隐患的直接有效的措施,也是我国食品安全监管工作的重要环节。作为食品检验的重要组成部分,样品管理直接兲乎到检测数据的公正性、科学性、准确性,对提高检测机构的权威性也具有重要意义。相兲人员大多对抽样的觃范性和科学性、检验技术的兇迚性和准确性非常兲注,对样品管理的重视程度相对不足。然而,由于各种因素的影响,食品检验中样品管理还存在诸多需要注意的问题,可能影响食品检验的准确性和有效性。本文对对样品接收、样品保存、样品制备等环节实施严栺管理迚行总结分析,以期降低检验风险。     

近红外光谱的苹果内部品质在线检测模型优化

利用近红外光谱技术在线检测水果内部品质的关键是获取精度高稳健性好的定量分析模型。研究开发了短波近红外光谱苹果品质在线检测系统,试验时苹果样本传输速度为5个/s,以漫反射方式采集,有效光谱范围为500~1100 nm。经光谱强度标准化校正后,有比较的采用遗传算法、连续投影算法和蚁群优化算法等提取特征变量,分别建立偏最小二乘模型,同时分析了这三种方法提取光谱特征变量的搜索机制。特征变量提取方法建立的预测模型所用变量显著减少,预测效果均优于全光谱模型,且能提高运算速度,增强模型的稳健性;其中又以蚁群优化算法的模型预测能力最佳,预测集相关系数R为0.9358,预测均方根误差RMSEP为0.2619。研究结果表明,近红外光谱结合特征变量提取方法可以建立高效的苹果可溶性固形物含量在线检测模型,在产业化应用方面具有很大潜力。     

变化光照条件下人眼定位方法

人脸识别是一种前沿的生物识别技术,作为脸部的重要特征,通过对人眼的定位,可以获取标准人脸,有效地提高人脸识别率。针对变化光照条件下人眼定位变得困难的问题,实现了一种人眼定位方法。首先通过retinex理论获取光照不变量;其次,利用边缘直方图描述符(EHD)得到人眼候选区;再次,使用支持向量机(SVM)和人眼概率分布(EPM)来确定人眼精确位置。为了进一步提高速度,提出一种基于haar-like特征的双眼筛选法,该方法和EHD构成多级筛选策略,大量减少了非人眼区域,加快了SVM的分类速度。实验表明,改进后的方法在保证较高检测率的同时具有较少的计算时间。     

Visual detection of apple bruises using AdaBoost algorithm and hyperspectral imaging

Hyperspectral imaging technique (400–1000 nm) was used for rapid and nondestructive recognition of bruises of apples. A total of 324 hyperspectral images were collected from 108 Fuji apples and the average spectral reflectance was extracted from the region of interest (ROI) of each image. The classification results of AdaBoost for the data pretreated by various existing methods were compared. Then, the correlation-based feature selection (CFS) algorithm was used to obtain characteristic wavelengths for reducing data redundancy. After pretreating with multiplicative scatter correction (MSC) and CFS, the average accuracy of the selected wavelengths was 97.63%. Then, an image processing algorithm based on the characteristic wavelengths selected before was proposed for the visual discrimination of bruises. This algorithm performed independent component analysis (ICA) transformation of the selected wavelengths, and chose the third component image of the ICA transform, then used adaptive threshold segmentation to obtain the bruise region of apples. The results showed that hyperspectral imaging technology could discriminate apple bruise, and this study can help to develop an online apple bruises detection system.