To obtain safe products, it is necessary to ensure the integrity of each link in the food chain, including animal feed, which is the first step in the chain. This highlights the need for rapid and non-destructive analytical tools, such as Near Infrared Spectroscopy (NIRS), to meet the levels of control currently required in the feed industry. The aim of this research was to evaluate the potential of incorporating, at online level in the plant, a new generation of NIRS sensors for the quality control of compound animal feeds. The results indicated that NIRS is suitable for on-line use in the feed industry, providing reliable, non-destructive and accurate analysis of feed quality parameters.
KEYWORDS: Near infrared spectroscopy, Portability, Sensors, In vivo imaging, Industry, Tissues, Testing and analysis, Nondestructive evaluation, Bone, Animals
In broiler breeder production, the level of abdominal fat is of utmost importance as it affects their reproductive performance. The industry relies on subjective palpation of the pelvic bones, which depends on the operator's experience, to assess subcutaneous fat and readiness for light stimulation. An alternative involves euthanizing the bird, but this is impractical for a large number of birds in a flock. Therefore, NIRS technology is postulated as a promising tool for in vivo determination of pelvic fat in pullet broiler breeders. This work aims to evaluate the use of NIR portable sensors for this purpose, attempting to optimize an efficient analysis methodology. The results suggest that NIRS technology offers a non-destructive and easy-to-use solution to improve in vivo assessments in the farm.
Hyperspectral Imaging (HSI) emerges as a non-destructive solution for assessing the quality of Iberian ham, a luxury Spanish product. Traditional quality controls involve costly and time-consuming chemical analysis and genotyping. HIS is a suitable tool to deal with such heterogeneous products, since it allows to acquire the whole surface of the sample and to know the spatial distribution of the main composition parameters, obtaining a more representative information. This study optimized a HSI system operating between 900-1700 nm for sliced Iberian ham quality assessment. After analyzing 104 samples, the most optimal region of interest for the subsequent development of prediction models was selected. Partial Least Regression (PLS) models were developed for the prediction of the content of salt, fat and proteins. The research demonstrates HSI's potential for fast, non-destructive quality evaluation, aiding producers in maintaining premium standards.
In the almond industry, the presence of bitter almonds in processed batches is a common problem that causes not only unpleasant flavors but also problems in the product commercialization. This research group has previously demonstrated the potential of Near Infrared Spectroscopy (NIRS) to detect adulterated almond batches; however, since NIRS provides an average spectrum of each batch, it does not enable to identify each individual bitter almond. Hyperspectral Imaging (HSI), which integrates both the spectral and spatial dimensions, enables to know the spatial distribution of the different physico-chemical characteristics, favoring the individual identification of the different compounds in the sample. The aim of this study was to evaluate the feasibility of using a HSI system for the identification of bitter almonds in sweet almond batches. Samples were analyzed using a HSI camera working in the spectral range 946.6–1648.0 nm and Partial Least Squares Discriminant Analysis (PLS-DA) was applied. A classification success over the 99% was obtained in cross-validation and the pixel-by-pixel validation identified correctly between the 61 – 85% of the adulterations. The results confirm that HSI can be considered a promising approach for the classification of almonds by bitterness, allowing the identification of each single bitter almond present in the batch.
Sharjah-Sat-1 is the first CubeSat to be designed and integrated at the Sharjah Academy for Astronomy, Space Sciences and Technology (SAASST), a research institute under the University of Sharjah (UoS) in the United Arab Emirates, with an active collaboration with Istanbul Technical University and Sabanci University in Turkey. The mission is due to launch in December 2022. Sharjah-Sat-1 hosts a primary payload of an improved X-Ray Detector (iXRD). The iXRD utilizes a CdZnTe crystal as an active detector to detect and measure bright and hard X-Ray sources and a tungsten collimator. The instrument’s detection range is 20-200 KeV at a spectral resolution of 6 Kev at 60 KeV [1]. The detector will be able to measure the flux of ionizing x-ray around the south Atlantic anomaly, the data of which will be shared to provide space situational awareness for other satellite operators to perform any preventative maneuvers to protect their space assets. This paper will discuss how the improved X-Ray Detector (iXRD) on-board the Sharjah-Sat-1 CubeSat can be utilized to provide space situational awareness.
Hyperspectral images are typically acquired at high spatial and spectral resolutions, being essential the reduction of data for the implementation of this technology at industrial level. The aim of this work was the optimization and development of algorithms for the selection of the region of interest in oranges hyperspectral data. PLS and its multilinear version, NPLS, were used to model the internal quality of oranges. The results obtained in external validation enabled to carry out a screening of the product according to its flavour, confirming that the use of multilinear models could reduce the noise and data redundancy.
Iberian pork meat has exceptional sensory and nutritional attributes, which are related to the breed and the feeding regime of the animals. Regarding the breed purity, two categories can be considered: 100% Iberian products and Iberian products coming from crossed animals (Iberian x Duroc). The aim of this work was to evaluate the viability of using portable Near-infrared sensors for the in situ authentication of Iberian pork fresh meat according to its breed. Models were developed using partial least squares discriminant analysis. The results confirm the viability of using NIRS to guarantee the authenticity of the Iberian pork meat.
Near infrared (NIR) spectroscopy can be a fast and reliable candidate for the non-destructive and in-situ classification of almonds by bitterness, when analysed in bulk. With that purpose, in-shell and shelled sweet and bitter almonds were analysed using a handheld diode array NIR spectrophotometer (950-1650 nm). Models were constructed using partial least squares-discriminant analysis (PLS-DA) and the optimum threshold value was set up using the Receiver Operating Characteristic (ROC) curves. The models correctly classified 95% of in-shell and 100 % of shelled samples belonging to the external validation sets. The excellent performances obtained for the classification models of the in-shell and shelled almonds analysed in bulk will enable to remove bitter almonds from the sweet almond batches and, with it, to avoid selling those batches containing bitter almonds that could lead to product depreciation.
Near Infrared (NIR) Spectroscopy is a powerful technology which can be implemented as a non-destructive tool to make decisions related to cultural practices and harvesting. However, prior to the incorporation of NIR sensors at field level as an analytical technique, a routine analysis procedure should be established. In this sense, this research is focused on the development of a methodology based on the use of a portable NIR instrument to monitor the growth process and to establish the optimum harvest time of spinach plants in the field. For this aim, calibration models for dry matter and nitrate contents were developed by means of Partial Least Squares (PLS) regression, using one spectrum per plant for dry matter content and nine spectra per plant for nitrate content taken with a portable spectrophotometer MicroNIR™ OnSite-W (908– 1676 nm). After that, to set a routine analysis methodology, the validation of the models was carried out using a single spectrum per plant selected at random and the suitability of the predictions was measured considering the Hotelling’s T2 statistic, whose control limit value was as inferior to 60. The results demonstrated that once the calibration models were developed, only one spectrum per plant will enable to predict successfully dry matter and nitrate contents. Therefore, the methodology established will allow to monitor spinach plants during their growth in the field based on internal quality and safety indexes.
The determination of the fatty acid profile in almonds has a huge interest to establish the nutritional value of the product. Hyperspectral Imaging (HSI) integrates both the spectral and spatial dimensions, enabling a rapid and non-destructive evaluation of the composition and distribution of quality indexes in agricultural products. The objective of this study was the determination of the two main unsaturated fatty acids -oleic and linoleic, in shelled almonds analysed in bulk using a HSI system working in the spectral range 946.6 to 1648.0 nm. The predictive models were developed using the mean spectrum extracted from the ROI of each sample and applying Partial Least Squares (PLS) regression. Subsequently, the external validation of the best models was carried out using the mean spectrum of each ROI and pixel-by-pixel. The results showed a good performance for the fatty acids analysed (R2cv = 0.78 and SECV = 2.17 for oleic and R2cv = 0.77 and SECV = 1.83 for linoleic), confirming the feasibility of using HSI as a non-destructive analytical tool to assess the lipid composition and its distribution in the almonds processed in bulk, as well as to include their nutritional properties in the labelling.
The increasing demand of the horticultural sector in terms of quality and safety assurance stresses the need of the producers and the agri-food industry of implementing non-destructive analysis techniques. Near infrared spectroscopy (NIRS) has proven to be an increasingly practical option for satisfying this demand. Recently a new generation of NIRS instruments has been developed, being necessary their previous evaluation before their incorporation for quality and safety assurance along the food supply chain. For this purpose, 230 summer squashes, grown outdoors in the province of Cordoba (Spain), were analyzed to determine quality (dry matter content (DMC) and soluble solid content (SSC)) and safety (nitrate content) parameters using two spectrophotometers, MicroNIRTM Pro 1700 and Matrix-F, ideally suited for the in situ and online analysis, respectively. A linear calibration strategy - modified partial least squares regression, MPLS - were used for the development of predictive models. The results obtained showed NIRS technology, by means of new generation sensors, is a potential tool for the non-destructive measurement of DMC (RPDcv = 1.76 and RPDcv = 1.98), SSC (RPDcv = 1.62 and RPDcv = 1.63) and nitrate content (RPDcv = 1.77 and RPDcv = 1.36), for the MicroNIRTM Pro 1700 and Matrix-F, respectively. This would enable to improve the quality and safety control of this vegetable throughout the whole supply chain, i.e. in field and in the processing plant.
The citrus sector is one of the most dynamic and important agricultural sectors. For the international market, it is of great interest the estimation of crop yield prior to harvest, since this yield estimation at the immature green stage could influence the future market price and allow producers to plan the harvest in advance. The aim of this work was to stablish the first steps to set up a methodology for the selection of the relevant bands to distinguish between green oranges and leaves and to detect external defects, which will allow citrus yield to be estimated on tree. Images were acquired from oranges and leaves from an orchard in Jeju island (Jeju, Republic of Korea), using a hyperspectral reflectance imaging system working in the range 400–1000 nm. Analysis of variance (ANOVA) and principal component analysis (PCA) were used to select the main wavelengths for this purpose; next, a band ratio coupled with a simple thresholding method was applied. The system correctly classified over the 90% of the pixels for both objectives, confirming that it is possible to use just few wavelengths to estimate harvest yield in oranges, although further studies are needed for the application of this system in the field, where other factors must be taken into account, such as sun-light illumination, shadows, etc. Therefore, this research can be considered as a preliminary step for designing a multispectral system capable of being mounted on unmanned aerial vehicles (UAVs) to estimate orange yield and defects.
Mandarin orange is a popularly consumed fruit in Asian countries. Over 99% of cultivation area in Korea for mandarin oranges is concentrated in Jeju Island. Despite of this high concentration, detecting infection and estimating fruit yields has been done manually, resulting in loss of money and time. In this study, hyperspectral fluorescence imaging technique was explored to distinguish green mandarin oranges from leaves to estimate fruit density. In addition, early stage detection for disease infection of leaves and fruits were investigated. The fluorescence spectral images showed reliable performance for distinguishing green mandarin oranges from leaves, and detecting disease infection on both leaves and fruits. The result demonstrated that hyperspectral fluorescence imaging might be used for rapid and non-destructive detection of disease infection and yield estimation of mandarin orange in the field.
Meat and bone meal (MBM) has been banned as animal feed for ruminants since 2001 because it is the source of bovine spongiform encephalopathy (BSE). Moreover, many countries have banned the use of MBM as animal feed for not only ruminants but other farm animals as well, to prevent potential outbreak of BSE. Recently, the EU has introduced use of some MBM in feeds for different animal species, such as poultry MBM for swine feed and pork MBM for poultry feed, for economic reasons. In order to authenticate the MBM species origin, species-specific MBM identification methods are needed. Various spectroscopic and spectral imaging techniques have allowed rapid and non-destructive quality assessments of foods and animal feeds. The objective of this study was to develop rapid and accurate methods to differentiate pork MBM from poultry MBM using short-wave infrared (SWIR) hyperspectral imaging techniques. Results from a preliminary investigation of hyperspectral imaging for assessing pork and poultry MBM characteristics and quantitative analysis of poultry-pork MBM mixtures are presented in this paper.
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