A review of optical nondestructive visual and near-infrared methods for food quality and safety

This paper is a review of optical methods for online nondestructive food quality monitoring. The key spectral areas are the visual and near-infrared wavelengths. We have collected the information of over 260 papers published mainly during the last 20 years. Many of them use an analysis method called chemometrics which is shortly described in the paper. The main goal of this paper is to provide a general view of work done according to different FAO food classes. Hopefully using optical VIS/NIR spectroscopy gives an idea of how to better meet market and consumer needs for high-quality food stuff.©2013 the Authors. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.fi=vertaisarvioitu|en=peerReviewed

Review of near-infrared spectroscopy as a process analytical technology for real-time product monitoring in dairy processing

peer-reviewedReal-time process/product monitoring can be achieved using suitable process analytical technologies (PAT) to improve process efficiencies and product quality. In the dairy industry, near infrared (NIR) spectroscopy has been utilised as a laboratory analytical method (off-line) for compositional analysis of dairy products since the 1970s. Recent advances in NIR technology and instrumentation have widened its applications from a bench-top analytical instrument to a promising PAT tool for on-line and in-line implementation. This review focuses on the use of NIR technology for real-time monitoring of dairy products, by briefly outlining the measurement principle, NIR instrument configurations, in-line sampling methods, calibration models development, some practical considerations for process installation, and current state of the art in on-line and in-line NIR applications (2012 to date) for continuous process monitoring in the production of dairy products. The challenges and additional resources required to improve production efficiencies using NIR spectroscopy are also discussed.Dairy Processing Technology Centre (DPTC) and Enterprise Irelan

The Effect of Light Intensity, Sensor Height, and Spectral Pre-Processing Methods When Using NIR Spectroscopy to Identify Different Allergen-Containing Powdered Foods

Food allergens present a significant health risk to the human population, so their presence must be monitored and controlled within food production environments. This is especially important for powdered food, which can contain nearly all known food allergens. Manufacturing is experiencing the fourth industrial revolution (Industry 4.0), which is the use of digital technologies, such as sensors, Internet of Things (IoT), artificial intelligence, and cloud computing, to improve the productivity, efficiency, and safety of manufacturing processes. This work studied the potential of small low-cost sensors and machine learning to identify different powdered foods which naturally contain allergens. The research utilised a near-infrared (NIR) sensor and measurements were performed on over 50 different powdered food materials. This work focussed on several measurement and data processing parameters, which must be determined when using these sensors. These included sensor light intensity, height between sensor and food sample, and the most suitable spectra pre-processing method. It was found that the K-nearest neighbour and linear discriminant analysis machine learning methods had the highest classification prediction accuracy for identifying samples containing allergens of all methods studied. The height between the sensor and the sample had a greater effect than the sensor light intensity and the classification models performed much better when the sensor was positioned closer to the sample with the highest light intensity. The spectra pre-processing methods, which had the largest positive impact on the classification prediction accuracy, were the standard normal variate (SNV) and multiplicative scattering correction (MSC) methods. It was found that with the optimal combination of sensor height, light intensity, and spectra pre-processing, a classification prediction accuracy of 100% could be achieved, making the technique suitable for use within production environments

Advances in Hyperspectral and Multispectral Optical Spectroscopy and Imaging of Tissue

The purpose of this SI is to provide an overview of recent advances made in the methods used for tissue imaging and characterization, which benefit from using a large range of optical wavelengths. Guerouah et al. has contributed a profound study of the responses of the adult human brain to breath-holding challenges based on hyperspectral near-infrared spectroscopy (hNIRS). Lange et al. contributed a timely and comprehensive review of the features and biomedical and clinical applications of supercontinuum laser sources. Blaney et al. reported the development of a calibration-free hNIRS system that can measure the absolute and broadband absorption and scattering spectra of turbid media. Slooter et al. studied the utility of measuring multiple tissue parameters simultaneously using four optical techniques operating at different wavelengths of light—optical coherence tomography (1300 nm), sidestream darkfield microscopy (530 nm), laser speckle contrast imaging (785 nm), and fluorescence angiography (~800 nm)—in the gastric conduit during esophagectomy. Caredda et al. showed the feasibility of accurately quantifying the oxy- and deoxy-hemoglobin and cytochrome-c-oxidase responses to neuronal activation and obtaining spatial maps of these responses using a setup consisting of a white light source and a hyperspectral or standard RGB camera. It is interest for the developers and potential users of clinical brain and tissue optical monitors, and for researchers studying brain physiology and functional brain activity

Investigation of Parameters That Affect the Acquired Near Infrared Diffuse Reflected Signals in Non-Destructive Soluble Solids Content Prediction

Near infrared spectroscopy is a susceptible technique which can be affected by various factors including the surface of samples. According to the Lambertian reflection, the uneven and matte surface of fruits will provide Lambertian light or diffuse reflectance where the light enters the sample tissues and that uniformly reflects out in all orientations. Bunch of researches were carried out using near infrared diffuse reflection mode in non-destructive soluble solids content (SSC) prediction whereas fewer of them studying about the geometrical effects of uneven surface of samples. Thus, this study aims to investigate the parameters that affect the near infrared diffuse reflection signals in non-destructive SSC prediction using intact pineapples. The relationship among the reflectance intensity, measurement positions, and the SSC value was studied. Next, three independent artificial neural networks were separately trained to investigate the geometrical effects on three different measurement positions. Results show that the concave surface of top and bottom parts of pineapples would affect the reflectance of light and consequently deteriorate the predictive model performance. The predictive model of middle part of pineapples achieved the best performance, i.e. root mean square error of prediction (RMSEP) and correlation coefficient of prediction (Rp) of 1.2104 °Brix and 0.7301 respectively

Applicability of Confocal Raman Microscopy to Observe Microstructural Modifications of Cream Cheeses as Influenced by Freezing

Confocal Raman microscopy is a promising technique to derive information about microstructure, with minimal sample disruption. Raman emission bands are highly specific to molecular structure and with Raman spectroscopy it is thus possible to observe different classes of molecules in situ, in complex food matrices, without employing fluorescent dyes. In this work confocal Raman microscopy was employed to observe microstructural changes occurring after freezing and thawing in high-moisture cheeses, and the observations were compared to those obtained with confocal laser scanning microscopy. Two commercially available cream cheese products were imaged with both microscopy techniques. The lower resolution (1 µm/pixel) of confocal Raman microscopy prevented the observation of particles smaller than 1 µm that may be part of the structure (e.g., sugars). With confocal Raman microscopy it was possible to identify and map the large water domains formed during freezing and thawing in high-moisture cream cheese. The results were supported also by low resolution NMR analysis. NMR and Raman microscopy are complementary techniques that can be employed to distinguish between the two different commercial formulations, and different destabilization levels

The frequency range in THz spectroscopy and its relationship to the water content in food: A first approach

The objective of this review is to report on the progress made so far in the development of THz spectroscopy technology with application in the food industry, as well as, to evaluate the range of frequencies used by this technology in relation to the water content of food, to find patterns in which the physicochemical characterization of food samples is most effective. From the literature reviewed, it has been found that THz spectroscopy is still in constant development, both in the physical part of the equipment and in the data processing techniques. Despite these advances, the frequency ranges in which the identification of compounds are influenced by the interference of the water composition of food have not been clearly identified, even molecular behavior of water in the frequency ranges corresponding to the spectral band of THz is still little known. When performing a meta-analysis of the data specifying the frequency ranges in relation to the water content of food samples, reported in the literature, two intervals have been identified, where the action of THz waves have a better response in terms of the quantification of water, as well as of other compounds, which are mainly evidenced in lower water content, explained by the mechanisms of water relaxation in response to the interaction of THz waves. This result suggests that the influence of water content on the quantification of compounds should be considered, as it may be under or overestimated