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NIR hyperspectral imaging methods for quality and safety control of food and feed products: Contributions to 4 European projects
... Considering the importance of accurately determining maize grade, an objective method must be developed that will recognise a wide range of defective and foreign materials important to farmers, traders and millers. Spectral imaging has been identified as a potential alternative to the current visual inspection methods (Gowen et al. 2007;Amigo et al. 2013). Hyperspectral imaging has been used in many food and agricultural applications, including studies on a wide range of cereal commodities and properties (Caporaso et al. 2018a;Sendin et al. 2018b). ...
... These include numerous studies of maize, including hardness prediction (McGoverin and Manley 2012;Manley et al. 2011;Williams et al. 2009), chemical content prediction (Cogdill et al. 2004;Weinstock et al. 2006;Vermeulen et al. 2017b), variety identification (Wang et al. 2015) and fungal detection (Del Fiore et al. 2010;Williams et al. 2012;Vermeulen et al. 2017a). It has also been applied in the quality and safety evaluation of wheat (Mahesh et al. 2015;Manley et al. 2011;Singh et al. 2010), rice (Del Fiore et al. 2010;Wang et al. 2014), fonio (Baeten et al. 2010) and sorghum and barley (McGoverin et al. 2011). ...
Near infrared hyperspectral imaging with multivariate image analysis was evaluated for its potential to grade whole white maize kernels. The study was based on grading regulations stipulated in South African legislation and aimed to provide an alternative to the tedious and subjective manual methods currently used. The types of undesirable materials regarded were divided into 13 classes and imaged using a hyperspectral imaging system (1118–2425 nm). Two approaches to data analysis, pixel-wise and object-wise, were investigated using principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) modelling. Two-way classification models distinguished sound white maize from each type of undesirable material and were validated with independent image datasets. The pixel-wise PLS-DA demonstrated a high occurrence of errors (63–99% classification accuracy). The object-wise PLS-DA models yielded superior results, achieving 100% classification accuracy in 8 of the 13 models, with the remaining 5 incurring only one error each (98% classification accuracy). The overall classification accuracy achieved over the total 804 kernels/objects was 99.4%. Important spectral features were highlighted around 1219 and 1476 nm (associated with starch), 1941 nm (associated with moisture) and 2117 nm (associated with protein). An object-wise approach demonstrated good performance for distinguishing between the sound maize class and common grading defects and provided a classification for single, whole maize kernels, as would be conducted during the current manual grading methods. For industry implementation, this system may be simplified to a multispectral system for reduced cost and higher throughput.
... absorbance at specific wavelengths). The most important HSI applications in the agro-food industry are in detecting defects, 8,9 discriminating botanical species, cultivars and quality classes, [10][11][12] determining fruit ripeness [7][8][9][10][11][12][13] and chemical composition, [14][15][16] and detecting and quantifying contaminants. 6,[17][18][19][20][21][22][23][24] Traditionally, hyperspectral instruments are classified into three groups, depending on the way the hypercube is generated: point-scan, line-scan, and plane-scan instruments. ...
... Several studies have reported on the use of HSI to predict, inter alia, the content of water, fat, protein, and total saturated and total unsaturated fatty acids in red meat [26][27][28][29] ; fat and protein content in cheese 30 ; moisture and fat content in various species of fish 31 ; soluble solids, moisture content, and acidity in fruits and vegetables [32][33][34][35] ; and moisture, fat, starch, and oleic acid content in cereals. 14,36,37 Studies have also been conducted to predict the content of minor compounds such as anthocyanins and flavonols in grape skin 38 and grape seed, 39 respectively; total pigment in red meat 40 ; synthetic astaxanthin coating and total volatile basic nitrogen in fish 41,42 ; and total glucosinolate in freeze-dried broccoli. 43 Macronutrients in oilseed rape leaves 44 and wheat leaves 45 have been also determined using HSI. ...
Hyperspectral imaging is a powerful technique that combines the advantages of near infrared spectroscopy and imaging technologies. Most hyperspectral imaging studies focus on qualitative analysis, but there is growing interest in using such technique for the quantitative analysis of agro-food products in order to use them as universal tools. The overall objective of this study was to compare the performance of a hyperspectral imaging instrument with a classical near infrared instrument for predicting chemical composition. The determination of the protein content of wheat flour was selected as example. Spectra acquisition was made in individual sealed cells using two classical near infrared instruments (NIR-DS and NIR-Perstop) and a near infrared hyperspectral line-scan camera (NIR-HSI). In the latter, they were also acquired in open cells in order to study the possibility of accelerating the measurement process. Calibration models were developed using partial least squares for the full wavelength range of each individual instrument and for the common range between instruments (1120–2424 nm). The partial least squares models were validated using the “leave-one-out” cross-validation procedure and an independent validation set. The results showed that the NIR-HSI system worked as well as the classical near infrared spectrometers when a common wavelength range was used, with an r² of 0.99 for all instruments and Root Mean Square Error in Prediction (RMSEP) values of 0.15% for NIR-HSI and NIR-DS and 0.16% for NIR-Perstop. The high residual predictive deviation values obtained (8.08 for NIR-DS, 7.92 for NIR-HSI, and 7.56 for NIR-Perstop) demonstrate the precision of the models built. In addition, the prediction performance with open cells was almost identical to that obtained with sealed cells.
... For several years now, alternative methods for detecting processed animal proteins were tested, including spectroscopy and proteomic methods Fumi ere et al. 2009;Baeten et al. 2010;Boix et al. 2012;Tena et al. 2014;Mandrile et al. 2017;Lecrenier et al. 2021). The aim of this paper is to discuss their specificities regarding the detection, identification and quantification of insect PAPs. ...
Since their approval for use in aquaculture in 2017, processed insect proteins have been extensively studied for their nutritional quality in animal feed. This new type of meal is highly promising but requires, as for other products used in animal feed, strict sanitary control in accordance with European legislation. Within this legal framework, light microscopy and PCR remain the official methods but have some analytical limitations that other methods could overcome. This paper aims to provide an overview of the European legislation concerning use of processed insect proteins, but also to highlight the advantages and disadvantages of the official methods for their analysis. It also points out other analytical methods, which have already proved their worth for the analysis of processed animal proteins, which could be used as complementary methods.
... Thus, NIRS spectroscopy methods can be used to capture the spectral information which allows the assessment of the chemical content of intact biological materials. Nearinfrared hyperspectral imaging (NIR-HSI) is a nondestructive technique that combines the conventional NIR spectroscopy and imaging techniques to obtain both spectral and spatial information from a sample (Chang, 2003;Baeten et al., 2010;Manley, 2014), thus, allowing the analysis of the chemical content of different parts of the same biological materials such as single seeds. ...
The edible seeds of bottle gourd [Lagenaria siceraria (Molina) Standl.] are rich in oils, proteins and minerals of high nutritional quality. They are highly prized in pan tropical regions where they constitute valuable resources for food and nutrition security. In this study, near-infrared hyperspectral imaging (NIR-HSI) was combined with chemometrics to assess the variability of seed chemical content of African cultivars for the selection of nutritional traits. Six hundred seeds of four accessions belonging to two cultivars were collected from the Ivory Coast (West Africa) and analysed. The NIR-HSI spectra collected on whole seeds in the 1100-2400 nm range revealed that the main absorption bands of the seed chemical content were associated with water, lipids and proteins. The absorbance values between seeds of the same accession in these spectral regions varied up to 1.8 folds. Among the two chemometric tools used, principal component analysis (PCA) did not separate the accessions while Partial Least Squares Discriminant Analysis (PLS-DA) discriminated the accessions with 87.33 % to 94.67 %, and the cultivars with 90 % to 92 % correct classification. Seed oils from bottle gourd are for instance rich in linoleic acid which is an essential fatty acid for human health. The non-destructive and qualitative determination of the content of single seeds was demonstrated in the study and provides the opportunity to select superior seeds for the improvement of key nutritional traits in bottle gourd. Lagenaria siceraria, near-infrared hyperspectral imaging, seed chemical content, PCA, PLS-DA, nutrition security
... Near infrared hyperspectral imaging (NIR-HSI) is a NIR-based technology that yields spectral and spatial information simultaneously (Baeten, Pierna, Vermeulen, & Dardenne, 2010;Dale et al., 2013;Fern� andez Pierna, Baeten, & Dardenne, 2006). Previous studies have shown the ability of NIR-HSI in tandem with multivariate analysis to Fig. 1a. ...
The hybridization of cocoa generates new varieties with the aim of opening new flavors, textures, and disease resistance. The objective of this study was to develop and validate classification models based on NIR hyperspectral imaging and chemometrics for the discrimination of five valuable cocoa bean hybrids. The chemometrics tools, PLS-DA and SVM, showed comparable results for two-class (hybrids) models, but SVM (3.8–23.1% prediction error) was superior to PLS-DA (4.4–34.4% prediction error) when all five classes (hybrids) were included in a model. PLS-DA maps showed a simple and informative way to discriminate hybrids, allowing a correct classification in 50–100% of cases. Finally, it can be concluded that the models created in this work could be a good and reliably alternative to the actual visual method for the discrimination of cocoa bean hybrids.
... There is a growing output of scientific papers describing the new technological advances and their applications (Wilson & Tapp, 1999; Cen & He, 2007). The spectroscopic approach can be applied both in laboratory basic research and in the factory as an on-line tool for monitoring food products, production processes, and quality (Baeten et al., 2010). ...
Consumers are more and more interested in food safety and quality. Various analytical techniques are proposed. Among them we can find vibrational spectroscopic techniques (e.g., NIR, MIR, and Raman). Food products have their specific composition which allows characteristic spectra considered as “fingerprint.” Spectroscopic techniques can be applied as a first approach to obtain basic knowledge about a food product as well as a way to undergo qualitative and quantitative analyses. These techniques have the advantage to be rapid, easy-to-use, and nondestructive in comparison to tedious reference chemical and classical techniques which require solvents and are time consuming. Moreover, combination with multivariate analyses makes it easier to extract the significant information from the huge collected data. Spectroscopy techniques allow both at-line and on-line analyses. They can be used at laboratory or in a production line; recent developments in on-line spectrometers make it possible to analyze a sample in a vessel, during a production process or in the field.
... 13 NIR-HIS is now also being widely used in the agricultural sector. 14,15 It has been applied in the development of rapid methods for accurate feed-component detection, 16,17 species-flora identification, 5,18,19 vegetation-stress detection 20 and quality control and bruise damage detection in fruits and vegetables, including apples, 21 strawberries, 22 tomatoes, 23 mushrooms 24 and cucumbers. 25 In the cereal sector, NIR-HIS has been used, inter alia, for assessing the degree of hardness in maize kernels, 26 studying fungal infestation, 27 tracking the diffusion of conditioning water in single wheat kernels, 28 detecting and quantifying spice adulteration, 29 characterising fonio millet, 4,30 as an alternative technique for genetically modified organisms (GMOs) 31 and for detecting and quantifying contaminants in cereals. ...
This study was aimed at exploring the feasibility of detecting and quantifying melamine, and the structural analogue cyanuric acid, contamination in soybean meal, using line-scan near infrared (NIR) hyperspectral imaging spectroscopy (HIS). Soybean meal is one of the main ingredients used in the feed industry because it offers a complete protein profile. Each year, demand increases for soybean products and soya oil, the consumption of which is directly boosted by Chinese consumers’ growing wealth, and for soybean meal, which is indirectly affected by the growing demand for meat. Recent cases of deliberate melamine contamination of soybean meal have been reported. This study focuses on the development of a methodology based on NIR–HIS for the acquisition, treatment and interpretation of images and spectra, as well as for the detection and quantification of melamine and cyanuric acid contamination in soybean meal. A total of 40 commercial soybean meal samples were collected, and 17 adulterated samples were prepared by adding different amounts
of melamine/cyanuric acid to the samples, with concentrations varying between 0.5% and 5%. The spectral data were collected using line-scan NIR–HIS, and a qualitative model was created based on a principal-component analysis (PCA), whereas partial least-squares discriminant analysis was used to obtain a discrimination model and a semi-quantitative prediction of the content of contaminant. This study has permitted the detection of low levels of melamine and also revealed some limitations for the feasibility of quantifying melamine in soybean meal.
... The International Association of Feedingstuff Analysis has used light microscopy and color reaction to identify the presence of ergot bodies [14]. More recently, spectroscopy-based techniques, i.e., NIR hyperspectral imaging, combined with chemometric tools, have been proposed for detecting contaminants in food and feed [15][16][17][18][19][20][21]. ...
In recent years, near-infrared (NIR) hyperspectral imaging has proved its suitability for quality and safety control in the cereal sector by allowing spectroscopic images to be collected at single-kernel level, which is of great interest to cereal control laboratories. Contaminants in cereals include, inter alia, impurities such as straw, grains from other crops, and insects, as well as undesirable substances such as ergot (sclerotium of Claviceps purpurea). For the cereal sector, the presence of ergot creates a high toxicity risk for animals and humans because of its alkaloid content. A study was undertaken, in which a complete procedure for detecting ergot bodies in cereals was developed, based on their NIR spectral characteristics. These were used to build relevant decision rules based on chemometric tools and on the morphological information obtained from the NIR images. The study sought to transfer this procedure from a pilot online NIR hyperspectral imaging system at laboratory level to a NIR hyperspectral imaging system at industrial level and to validate the latter. All the analyses performed showed that the results obtained using both NIR hyperspectral imaging cameras were quite stable and repeatable. In addition, a correlation higher than 0.94 was obtained between the predicted values obtained by NIR hyperspectral imaging and those supplied by the stereo-microscopic method which is the reference method. The validation of the transferred protocol on blind samples showed that the method could identify and quantify ergot contamination, demonstrating the transferability of the method. These results were obtained on samples with an ergot concentration of 0.02 % which is less than the EC limit for cereals (intervention grains) destined for humans fixed at 0.05 %. [Figure not available: see fulltext.]
... All these applications contribute to a more precise knowledge of life science mechanisms. Of particular interest from a public health perspective are instruments designed for multispectral or NIR hyperspectral imaging analysis that already play a key role in automatic food and feed inspection [16][17][18][19][20][21][22] and will continue to do so. In recent years, NIR hyperspectral imaging has demonstrated its suitability for quality and safety control in the feed sector; it has been used successfully, for example, in the detection of processed animal proteins in feed [23][24][25][26], as well as in the cereal sector [27][28][29][30][31][32] where it enables rapid collection of a multitude of spectra of individual kernels or particles, which is of great interest for laboratories involved in the control of compound feed or cereals. ...
Food and feed safety as well as quality control analyses are often carried out using reference methods that have limitations in terms of adequation for the optimum implementation at the different steps of the food/feed chain and for the control of the end-products. Recent developments in analytical instrumentation and data processing methods have led to increased use of spectroscopic techniques, being proposed to establish alternative methods replacing the reference techniques. In recent years, these improvements have included the development of NIR hyperspectral imaging methods combined with appropriated chemometric tools. The research aim of this paper is to show that combining NIR hyperspectral imaging spectroscopic technique with chemometrics can greatly improve food and feed safety and quality control. For this purpose, two case studies were conducted using two different NIR hyperspectral imaging systems combined with chemometric tools and spectral rules applied in a dichotomist way. The first study focused on the detection of impurities in cereals in order to integrate a complete methodology into an automatic cereal selection or production chain. The second study focused in the contamination of plants by pathogens and showed the potential of this combination for detecting and quantifying cyst nematodes in sugar beet roots.
... The current study proposes an alternative method for detecting and quantifying ergot bodies in cereals using an online NIR hyperspectral imaging system combined with some chemometric tools (Baeten and Dardenne 2005;Baeten et al. 2007Baeten et al. , 2010Vermeulen et al. 2010Vermeulen et al. , 2011. This technique is faster than the classical microscopic method and enables a large quantity of material to be analysed, thereby avoiding the sampling problems associated with representative sampling. ...
... The current study proposes an alternative method for detecting and quantifying ergot bodies in cereals using an online NIR hyperspectral imaging system combined with some chemometric tools (Baeten and Dardenne 2005;Baeten et al. 2007Baeten et al. , 2010Vermeulen et al. 2010Vermeulen et al. , 2011. This technique is faster than the classical microscopic method and enables a large quantity of material to be analysed, thereby avoiding the sampling problems associated with representative sampling. ...
The occurrence of ergot bodies (sclerotia of Claviceps purpurea) in cereals presents a high toxicity risk for animals and humans due to the alkaloid content. To reduce this risk, the European Commission fixed an ergot concentration limit of 0.1% in all feedstuffs containing unground cereals, and a limit of 0.05% in 'intervention' cereals destined for humans. This study sought to develop a procedure based on near infrared hyperspectral imaging and multivariate image analysis to detect and quantify ergot contamination in cereals. Hyperspectral images were collected using an NIR hyperspectral line scan combined with a conveyor belt. All images consisted of lines of 320 pixels that were acquired at 209 wavelength channels (1100-2400 nm). To test the procedure, several wheat samples with different levels of ergot contamination were prepared. The results showed a correlation higher than 0.99 between the predicted values obtained using chemometric tools such as partial least squares discriminant analysis or support vector machine and the reference values. For a wheat sample with a level of ergot contamination as low as 0.01 %, it was possible to identify groups of pixels detected as ergot to conclude that the sample was contaminated. In addition, no false positives were obtained with non-contaminated samples. The limit of detection was found to be 145 mg/kg and the limit of quantification 341 mg/kg. The reproducibility tests of the measurements performed over several weeks showed that the results were always within the limits allowed. Additional studies were done to optimise the parameters in terms of number of samples analysed per unit of time or conveyor belt speed. It was shown that ergot can be detected using a speed of 1-100 mm/s and that a sample of 250 g can be analysed in 1 min.
Within the Green Analytical Chemistry paradigm, the best alternative for greening the methods of analysis comprises the direct determination of sample composition and/or sample properties without any chemical sample treatment, and, of course, it would be preferable not to have any physical treatment before acquisition of the information desired.In this review, we evaluate the state of the art in, and available alternatives for, analysis of solids without prior sample treatment, starting from the non-invasive quality control of products or processes through to the use of portable instruments for in situ determinations and the development of methods based on image analysis. We evaluate the advantages of such methodologies from the viewpoints of both environmental and operator risk.
For decades, Near InfraRed Spectroscopy (NIRS) has been widely used in the food and feed industry in order to implement rapid, relatively inexpensive and efficient control tools to assure the quality of products. NIRS is a branch of the molecular vibrational spectroscopy that refers to the measurement of radiation intensity (i.e. absorbance) as a function of frequency ranging in the electromagnetic spectrum (usually expressed as a function of wavelength [nm], but with the introduction of Fourier transform based instrument also as a function of wavenumber [cm-1]) in the
780-2500 nm (12820 – 4000 cm-1).
In order to identify and assess the barley varieties, a large number of analytical methods have been developed: visual examination of the kernel morphology, simple laboratory tests and measures, more elaborated and slower methods as protein or DNA detection and non destructive and rapid methods.
The trials for barley registration on the Belgium catalog studied at the CRA-W offered a real opportunity for a variety discrimination study. Three hundred seventy-eight barley samples are available. They are issued from different trials planned on three years and from seven Belgium locations. The varieties tested are winter and spring barley, 2 and 6 rows barley including malting and feed grade barleys. These samples have been selected in such a way that any variation due to climate, geographical location or agronomy in Belgium is included.
All those varieties were first observed in the field and then the samples were analysed at the laboratory. Information regarding diseases, height, earliness, yield and component yield, have been collected as well as technological analyses by using reference methods and spectroscopic methods. Beside those classical analyses, a recent and more sophisticated technique is also applied: the kernel by kernel analysis using the NIR imaging method.
The objective of this study is to propose a fast and reliable method for the discrimination of varieties. This is essential for establishing an efficient system for the traceability and quality control required in the seed sector as well as in the food and feed sectors. This work is partly undertaken in the framework of the FP6 CO-EXTRA project and the first results of the researches undertaken by the CRA-W allow to define the potential ability in variety discrimination by analysing kernel by kernel using NIR hyperspectral imaging spectrometers.
Directive 2002/32/EC of the European Parliament and of the Council of 7 May 2002 on undesirable substances in animal feed lists a range of substances from botanical origin (weed seeds) and additionally some chemical compounds directly originating from specific weeds. In order to examine the actual status of enforcement and of the present occurrence of these botanical substances, a survey was carried out. A questionnaire was sent to 103 laboratories, including official control labs from all member states of the European Union. The results, indicating the frequency of occurrence as far as reported, are compared to the publications of the EU Rapid Alert System for Food and Feed (RASFF). A total of 44 questionnaires was returned (42.7%) from 22 member states. Ten member states predominantly from north-western Europe appeared to have an active monitoring of botanical undesirable substances. The questionnaire results did not indicate that the other member states enforce this part of Directive 2002/32/EC. Reports on the frequency of occurrence include: a few to 25-50% of the samples contain traces of ergot (8 member states), a few to 24% contain at least some traces of thorn apple (6 member states), zero to 17% contain some castor oil plant seeds (3 member states), zero to a few samples contain Crotalaria seeds (3 member states), and zero to 6% contain traces of Sareptian mustard (4 member states). One member state conducted extra surveillance since several cases of animal intoxications have been reported. In some cases a coincidence with undesirable botanical substances was found.
IntroductionSample Characterization and Chemical Species DistributionDetecting Contamination and Defects in Agro-food ProductsOther Agronomic and Biological ApplicationsConclusion
References
This paper presents a framework for developing and validating a near infrared (NIB) hyperspectral imaging method as a standard protocol accepted by regulatory authorities. The focus is on detecting processed animal proteins (PAPs) in feedstuffs. Studies aimed at determining the performance characteristics and robustness of the protocol are presented. Various criteria and tests were used to assess the limit of detection, the repeatability and the risk of cross-contamination and to validate the NIR hyperspectral imaging method for detecting PAPs in compound feed. This protocol has been fully tested and validated though different studies in line with International Standard ISO 17025 and it is essential in order to transfer the method to other laboratories and introduce this technology to official control at the laboratory level.
GMo detection using NIR and chemometrics
- J A Pierna
- V Baeten
- Ph
- T Vermeulen
- G Buhigiro
- E Berben
- P Janssen
- Dardenne
J.A. Fernández Pierna, V. Baeten, Ph. Vermeulen, T. Buhigiro, G. Berben, E. Janssen
and P. Dardenne, "GMo detection using
NIR and chemometrics", 13th International
Conference on Near Infrared Spectroscopy,
umea, Sweden, 15-21 June 2007 (2007).