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.
International standards for Olive Oil (OO) analysis face challenges, especially with the rampant adulteration of Extra Virgin Olive Oil (EVOO). As demands grow for innovative methods beyond conventional techniques, VIS + NIRS spectroscopy emerges prominently. This study postulates enhanced efficacy through integrating VIS + NIRS with Fluorescence spectroscopy. Addressing challenges like instrument optimization and data security is paramount. Our research evaluates a hand-held multi-mode spectroscopy system combining fluorescence and reflectance. Employing three prototypes, we analyzed EVOO, VOO, and LOO categories. Results, compared against a benchtop instrument, provide insights into tackling EVOO adulteration through advanced spectral sensing.
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.
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.
The European Commission has stablished standards to be applied to all the olive oils subject to international trade. The standards include what is called conformity checks to know the physico-chemical and organoleptic properties, for labelling and category requirements (Delegated Regulation (EU) 2022/2104). However, most of those methods are inaccessible to most producers and retailers. Furthermore, the high cost and time required to obtain the data means that the number of samples inspected per year is very low in relation to the total volume of olive oils produced. There is therefore a growing and urgent demand for novel, fast and low-cost analytical methods to guarantee the authenticity and integrity of olive oils. The present work will provide scientific evidence of the potential of a handheld Linear Variable Filters (LVF) NIRS instrument for the on-site quality control of olive oils.
This work tries to demonstrate the potential of Near Infrared Spectroscopy combined with class-modelling and discriminant methods, for improvement of EVOO authentication. To cover that goal, 209 olive oil samples from the three mentioned categories (68 EVOO, 93 VOO and 48 LOO) were analyzed in a FT-NIR instrument coupled to an in-line fiber optic probe. The best models developed allow to classify correctly 82% of samples as EVOO and 84.93% as VOO. These results show that NIRS technology can be a great instrumental method to replace/complement the Panel Test.
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.
Software tools for chemometric analysis of NIRS data have existed since the first NIRS instruments appeared on the market in the late 1970s. Generally, these software appear attached to a certain instrumentation. Recently, some works have started to use open-source software, such as R and Python, but the development status is still in its infancy, particularly in the case of the latter. This work tries to generate information on the potential of the open-source Python software for the implementation of multivariate algorithms and signal pre-treatment methods for the quantitative and qualitative NIRS analysis of olive oils.
The uptake by the industry of the existing knowledge about the online NIR analysis is being much slower, compared to the acceptance of the at-line analysis. The Research Group of the authors since 2001 has been in close collaboration with the largest Spanish rendering plant to evalate the ability of different for the quality control of animal protein processed by-products. Since 2017, and after several years of research, the company decided to invest in a on-line project. The work done until, for moving from at line to on line analysis in the rendering plant will be summarised in the Conference.
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 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.
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.
Feeding dairy cows with Total Mixed Rations (TMR) is a cost-effective way to obtain high milk yield. Animal nutritionists are demanding accurate information on the main chemical constituents of TMR to properly feed lactating cows. The use of portable NIRS devices could provide an affordable answer. This work analysed a total of 121 TMR using two portable NIRS instruments for the prediction of dry matter, crude protein and neutral detergent fibre. The paper evaluated whether there were significant differences between the predictive capacities of the models developed from analytical data expressed “ as dry matter” or “ as is basis”.
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.
Conventional methods for the determination of chemical parameters of the fruit like soluble solids and acid content are often complicated and destructive, cannot be run on a large scale and are still far away from being implemented to large volumes of products or even better to individual piece fruits. In this study, the potential of hyperspectral imaging was evaluated for quantifying solid soluble content (SSC) and titratable acidity (TA) in intact oranges. Hyperspectral images (900–1700 nm) of 264 oranges collected during 2017 and 2018 at different maturation stages in Southern Spain farms were recorded. Partial least-squares analysis (PLS), Artificial Neural Network (ANN), optimized Support Vector Machine (SVM) and Gaussian Process Regression (GPR), as well as different spectral pre-processing methods, were tested for their effectiveness in quantifying titratable acidity (TA) and solid soluble content (SSC) in intact oranges. Random samples were chosen to validate the models by cross-validation. The best-selected models were then applied to a validation set of “unknown” samples and standard errors of prediction as well as correlation coefficients between actual and predicted values were calculated. Finally, a prediction map was developed to display the concentration distribution of the TA and SSC in the orange fruit, demonstrating that hyperspectral imaging (HSI) technique was feasible to quantify parameters in citrus fruit and can be further used for monitoring the quality of oranges at pre- and post-harvest in real-time.
The research published on animal protein by-products (ABPs) has been conducted using at- line instruments. The aim of this study is to evaluate different strategies to transfer a large spectral database of ABPs recorded in a monochromator instrument, to a FT-NIR instrument coupled to a fibre optic probe of 100 metres length, for the on-site quality control. The results obtained demonstrated that, once a large spectral data base of ABPs (more than 1300 samples) has been transferred from the monochromator to the FT-NIR instrument, the calibrations developed for on-site analysis have similar accuracy that those used previously for at-line analysis.
Currently, it is very demanded by nutritionists the availability of real-time on farm analysis for Total Mixed rations ( TMR ) quality control at the level of individual dairy farms. This study refers to the prediction of Crude Protein ( CP) in TMR, after transference of a library file ( N =394 ) of TMR samples from a monochoromator instrument, to two on-farm portable instruments (NIR4Farm, AUNIR, UK and AURORA, GraiNIT, Italy). The results obtained demonstrated that CP can be predicted by NIRS at “ on farm level”, with an accuracy similar to the most expensive at-line laboratory instruments.
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.
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.
Acorn Iberian ham (Jamón Ibérico de Bellota) is one of the most expensive luxury foodstuffs produced in Europe, with a highly appreciated smell and flavour. Its recognized high-sensorial quality and health properties are mainly due to the traditional outdoor feeding system (Montanera) of Iberian pigs (IP), which provides high standards of animal welfare. Nowadays, one of the frauds affecting this product is the use of “special compound feeds” to simulate the fat composition of the acorns through the inclusion of sources of oleic acid like the ones found in pigs fed outdoors. The high prices paid for a cured leg of Iberian ham –ranging from hundreds to thousands of euros- leads to many opportunities for mislabelling and fraud. Fatty acid content of the adipose tissue could provide evidence of the feeding system. Gas chromatography (GC) is used at industry level for production control purposes. However, it is costly and time-consuming, and it is only applied to batches of animals rather than individual pigs. The main goal of this study was to use spectra belonging to a portable NIRS instrument (MicroNIR Onsite Lite, Viavi Solutions Inc.) for on–site quantitative (fatty acid content) analysis of individual Iberian pork carcasses at the slaughterhouse. Performance of this portable instrument was compared with an at-line NIRS monochromator. PLS models were built and optimized resulting in standard errors of cross validation ranging from 0.83 to 0.84 for palmitic acid, 0.94 to 0.99 for stearic acid, 1.47 to 1.56 for oleic acid and 0.53 to 0.58 for linoleic acid.
KEYWORDS: Near infrared spectroscopy, Statistical analysis, Spectral resolution, Spectroscopy, Tissues, Databases, Matrices, Statistical modeling, Communication and information technologies, Satellites
This research is framed within FoodIntegrity, EU sponsored project(7th FP). The main goal of the research to be done is to provide industrials, producers and consumers with a methodology based in low-cost, portable and miniature NIRS sensors and information and communication technologies for process control and voluntary labelling, to guarantee the integrity of the EU high added-value as the “acorn Iberian pig ham”. The present study is focussed in transferring a database (470 samples) of IP tissue - analysed in a FOSS-NIRSystems 6500 (FNS6500) spectrometer, during the seasons 2009-2011 - to a portable/miniature instrument MicroNIR-Onsite, VIAVI (MN1700). A set of 30 samples of adipose tissue was taken from a slaughterhouse during 2015-2016, being analysed in parallel in the satellite (FNS 6500) and master (MN 1700) instruments. Latter on, they were divided in two sets: N = 10 for building the standardization matrices and N = 20 for the validation of the cloning procedure. The algorithm Piece-Wise Direct Standardization (PDS) was applied. The best standardisation matrix was applied to the library of 470 samples taken in the FNS 6500, enabling an excellent fitting between both instruments, as shown the RMSCs statistic calculated in the satellite before and after the standardization and in the master - 108457 vs 22519 vs 17646 μlog 1/R – and the GH distance before and after standardisation between both instruments 437.41 vs 2.06.
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.
This paper reports the chemometric analysis of near-infrared spectra drawn from hyperspectral images to develop, evaluate, and compare statistical models for the detection of beef in fish meal. There were 40 pure-fish meal samples, 15 pure-beef meal samples, and 127 fish/beef mixture meal samples prepared for hyperspectral line-scan imaging by a machine vision system. Spectral data for 3600 pixels per sample, in which individual spectra was obtain, were retrieved from the region of interest (ROI) in every sample image. The spectral data spanning 969 nm to 1551 nm (across 176 spectral bands) were analyzed. Statistical models were built using the principal component analysis (PCA) and the partial least squares regression (PLSR) methods. The models were created and developed using the spectral data from the purefish meal and pure-beef meal samples, and were tested and evaluated using the data from the ROI in the mixture meal samples. The results showed that, with a ROI as large as 3600 pixels to cover sufficient area of a mixture meal sample, the success detection rate of beef in fish meal could be satisfactory 99.2% by PCA and 98.4% by PLSR.
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