Near-infrared spectroscopy and imaging in food quality and safety

Over the last two decades, near-infrared spectroscopy (NIRS) has established itself as a non-destructive analytical technique in a variety of disciplines. However, recent technological advancements in hardware design and data mining techniques have unleashed the potential of NIRS to become a tool of choice for routine analyses of agricultural products. The current paper synthesizes the status of NIRS in the agri-food industry in terms of hardware and software development as well as the direction in which the NIRS research is headed. An extensive review of literature reveals that the emphasis on hardware development is focused on developing compact, robust, and portable spectrometers and hyperspectral imaging (HSI) systems. The software development on the other hand is geared towards developing better preprocessing, analyses, and modeling techniques using chemometrics, support vector machines, and artificial neural networks. The four main agri-food sectors identified to be the beneficiaries of this research revolution are grain quality monitoring; post-harvest handling of fruits and vegetables; identification of contaminants in animal produce and feed; and food safety and authenticity. Apart from discussing the aforementioned topics, the paper also provides food scientists some working knowledge on parameters crucial to the performance of spectral and imaging systems. It is expected that further development of NIRS will help agricultural and food scientists to enhance the quality and safety of our food.     

Quality evaluation of pickling cucumbers using hyperspectral reflectance and transmittance imaging: Part I. Development of a prototype

This article reports on the development of a hyperspectral imaging prototype for online evaluation of external and internal quality of pickling cucumbers. The prototype consisted of a two-lane round belt conveyor, two illumination sources (one for reflectance and one for transmittance), and a hyperspectral imaging unit. It had a novel feature of simultaneous imaging under reflectance mode in the visible region (400–675 nm) and transmittance mode for the red and near-infrared region (Red-NIR) (675–1000 nm). Reflectance information from the visible region was intended for evaluating the external characteristics of cucumbers such as skin color, whereas transmittance information from Red-NIR was used for internal defect detection (i.e., hollow center). Additional features of the prototype included simultaneous acquisition of reflectance and transmittance from calibration references that were installed in the system, to provide real-time, continuous corrections of individual hyperspectral images from each sample. Methods and algorithms were developed of estimating cucumber fruit size and correcting the effect of fruit size on transmittance measurements. The system was calibrated and evaluated for detecting the color, size, and internal defect of pickling cucumbers.     

Detection of early bruises in apples using hyperspectral data and thermal imaging

The early detection of bruises in apples was studied using a system that included hyperspectral cameras equipped with sensors working in the visible and near-infrared (400–1000 nm), short wavelength infrared (1000–2500 nm) and thermal imaging camera in mid-wavelength infrared (3500–5000 nm) ranges. The principal components analysis (PCA) and minimum noise fraction (MNF) analyses of the images that were captured in particular ranges made it possible to distinguish between areas with defects in the tissue and the sound ones. The fast Fourier analysis of the image sequences after pulse heating of the fruit surface provided additional information not only about the position of the area of damaged tissue but also about its depth. The comparison of the results obtained with supervised classification methods, including soft independent modelling of class analogy (SIMCA), linear discriminant analysis (LDA) and support vector machines (SVM) confirmed that broad spectrum range (400–5000 nm) of fruit surface imaging can improve the detection of early bruises with varying depths.     

Grading and color evolution of apples using RGB and hyperspectral imaging vision cameras

The potential of RGB digital imaging and hyperspectral imaging (900–1700 nm) was evaluated for discriminating maturity level in apples under different storage conditions along the shelf-life. Segmentation, preprocessing and partial least squares-discriminant analysis (PLS-DA) were used for hyperspectral data analysis, while illumination correction, dimensionality reduction and linear discriminant analysis (LDA) were used for RGB data analysis. The results showed that hyperspectral discrimination classified different storage regimes better than RGB, with an overall success rate of 95.83%. In addition, color evolution of apples during shelf-life under different storage regimes was modeled using RGB zero and first order regression models, fitting better to a first order kinetic model.     

Simultaneous determination of tenderness and Escherichia coli contamination of pork using hyperspectral scattering technique

A rapid nondestructive method based on hyperspectral scattering technique for simultaneous determination of pork tenderness and Escherichia coli (E. coli) contamination was studied in the research. The hyperspectral scattering images of thirty-one pork samples were collected in 400-1100 nm, and the scattering profiles were then fitted by Lorentzian distribution function to give three parameters a (asymptotic value), b (peak value) and c (full width at b/2). The combined parameters of (b-a), (b-a) × c, (b-a)/c and “a&b&c” were used to develop multi-linear regression (MLR) models for prediction of pork tenderness and E. coli contamination. It was shown that MLR models developed using parameters a, b, (b-a) and (b-a)/c can give high correlation coefficients of 0.831, 0.860, 0.856 and 0.930 respectively for pork tenderness prediction. For E. coli contamination of pork, MLR models based on parameters a and “a&b&c” can give high R_CV of 0.877 and 0.841 respectively.     

Multivariate image analysis: A review with applications

Nowadays, image analysis is becoming more important because of its ability to perform fast and non-invasive low-cost analysis on products and processes. Image analysis is a wide denomination that encloses classical studies on gray scale or RGB images, analysis of images collected using few spectral channels (sometimes called multispectral images) or, most recently, data treatments to deal with hyperspectral images, where the spectral direction is exploited in its full extension. Pioneering data treatments in image analysis were applied to simple images mainly for defect detection, segmentation and classification by the Computer Science community. From the late 80s, the chemometric community joined this field introducing powerful tools for image analysis, which were already in use for the study of classical spectroscopic data sets and were appropriately modified to fit the particular characteristics of image structures. These chemometric approaches adapt to images of all kinds, from the simplest to the hyperspectral images, and have provided new insights on the spatial and spectroscopic information of this kind of data sets. New fields open by the introduction of chemometrics on image analysis are exploratory image analysis, multivariate statistical process control (monitoring), multivariate image regression or image resolution. This paper reviews the different techniques developed in image analysis and shows the evolution in the information provided by the different methodologies, which has been heavily pushed by the increasing complexity of the image measurements in the spatial and, particularly, in the spectral direction.     

AD 4D Hyperlocal Would Like to Use Your Current Location

The physical nature of current location-tracked mobile culture can open up new sites to explore our relationship to place and question our cultural reliance upon it. Recent projects by London-based multidisciplinary design studio UniversalAssemblyUnit playfully offer a shared-ownership alternative to commercially driven centralised networks. The studio's cofounders Will Gowland and Samantha Lee here outline three such projects, involving a weather visualiser for Alaska, a laser communication network for Mexico City and a virtual replica of woodland for East Sussex.     

机载激光雷达数据处理质量控制与解决方案探讨

随着机载激光雷达技术的迅速发展,其数据处理已经成为整个生产过程的一个重要环节。文章就激光雷达数据处理过程中的GPS差分解算、坐标转换、点云数据分类、影像连接点、最终产品等的质量控制与解决方案进行分析和探讨。     

VIS/NIR spectroscopy for non-destructive freshness assessment of Atlantic salmon (Salmo salar L.) fillets

Visible/near-infrared spectroscopy has been evaluated for use in freshness prediction and frozen-thawed classification of farmed Atlantic salmon fillets, where fresh samples were stored as whole fish in ice. A handheld interactance probe for performing rapid measurements of single fillets and an imaging spectrometer for online analysis at an industrial speed of one fillet per second, have been used. Freshness as storage days in ice is predicted with an accuracy of 2.4 days for individual fillets, whereas frozen-thawed salmon fillets are completely separated from fresh fillets. The prediction results are comparable to previous results using the Quality Index Method with trained panelists. The region between 605 and 735 nm, which excludes interference by carotenoids and water, is appropriate for both frozen-thawed classification and freshness prediction of salmon fillets. The results indicate that the spectral changes are explained mainly by oxidation of heme proteins during the freeze–thaw cycle and during chilled storage in ice.     

Deciphering the Origins of P1-Induced Power Losses in Cu(Inx,Ga1–x)Se2 (CIGS) Modules Through Hyperspectral Luminescence

In this report, we show that hyperspectral high-resolution photoluminescence mapping is a powerful tool for the selection and optimization of the laser ablation processes used for the patterning interconnections of subcells on Cu(In_x,Ga_1−x)Se₂ (CIGS) modules. In this way, we show that in-depth monitoring of material degradation in the vicinity of the ablation region and the identification of the underlying mechanisms can be accomplished. Specifically, by analyzing the standard P1 patterning line ablated before the CIGS deposition, we reveal an anomalous emission-quenching effect that follows the edge of the molybdenum groove underneath. We further rationalize the origins of this effect by comparing the topography of the P1 edge through a scanning electron microscope (SEM) cross-section, where a reduction of the photoemission cannot be explained by a thickness variation. We also investigate the laser-induced damage on P1 patterning lines performed after the deposition of CIGS. We then document, for the first time, the existence of a short-range damaged area, which is independent of the application of an optical aperture on the laser path. Our findings pave the way for a better understanding of P1-induced power losses and introduce new insights into the improvement of current strategies for industry-relevant module interconnection schemes.