Empleo de la espectroscopia VIS NIR para la identificación de trazas de cacahuete en productos alimentarios en polvo

En el mundo existen ciertos grupos de población que muestran una hipersensibilidad a determinados alimentos, y cuya ingestión accidental desencadena, una respuesta del tipo “shock” anafiláctico. Esto ha obligado a las empresas alimentarias a estudiar de forma exhaustiva la gestión del riesgo de todos sus productos. El cacahuete es uno de los principales alérgenos en la industria. La espectroscopia NIR se ha utilizado recientemente para analizar la cantidad total de aceite y ácido grasos en cacahuete intacto (Sudaram y colaboradores, 2012). El objetivo de este trabajo es estudiar métodos no destructivos basados en espectroscopia para la detección de trazas de cacahuete en alimentos en polvo, como complemento al método genético reacción en cadena de la polimerasa en tiempo real (Real Time -PCR) desarrollado por el grupo de investigación TRADETBIO de la UCM, en el marco de colaboración en el Campus de Excelencia Internacional Moncloa. Los materiales utilizados fueron cacahuetes de cinco variedades de origen geográfico distinto y sometidas a diferentes tratamientos, proporcionadas por el Instituto de Materiales de Referencia CE, así como leche en polvo, cacao, harina de trigo, y cacahuete de diferentes marcas comerciales. Para todos ellos, se adquirieron dos series de espectros: en el infrarrojo cercano NIR (896-1686 nm), y los extraídos de imágenes hiperespectrales HIS (400-1000nm). La espectroscopia VIS se mostró sensible a las diferencias en el cacahuete en cuanto a su origen y/o tratamiento, ya que inducen cambios en el color, siendo inviable la separación entre los cacahuetes blanqueados, la leche y la harina en esta región espectral. Las principales diferencias entre los cacahuetes y el resto de ingredientes alimentarios se han encontrado en el rango NIR, específicamente en las longitudes de onda de (1207-1210 nm), relacionadas con una región de absorción de los lípidos. El infrarrojo permite 100% de segregación de cualquier tipo de cacahuete respecto al resto de los ingredientes alimentarios. La espectroscopia NIR combinada con las técnicas de imagen (hiperespectral o multiespectral) podría por tanto, ser aplicado para detectar trazas de cacahuetes en alimentos en polvo, no influyendo su origen y/o tratamiento, ya que es capaz de separar cualquier cacahuete del resto de los ingredientes alimentarios. Este método podría ser una técnica de cribado previo al método PCR de elevado coste.

Diseño de la red sísmica digital por satélite Haitiana

En 2010, Recursos Naturales Canada (RNC) instaló 3 estaciones sísmicas con transmisión vía satélite en Jacmel, Léogane y Puerto Príncipe, la capital de la Republica de Haití. Dicha instalación tenía como objetivo la monitorización de las réplicas del terremoto del 12 de enero del mismo año. Con el objetivo de ampliar la cobertura de la monitorización sísmica a todo el país y tener un centro de control propio, el Observatorio Nacional de Medio Ambiente y de la Vulnerabilidad (ONEV) del Ministerio del Medio Ambiente de Haití (MDE) compró 4 estaciones sísmicas completas con transmisión vía satélite de Nanometrics Inc. y el software correspondiente. Desafortunadamente, no se está sacando provecho de dichas estaciones compradas. En la actualidad, dos de ellas, que están instaladas en Hinche y Cabo Haitiano, no están configuradas, y las otras siguen en el almacén del ONEV. No se ha conseguido el presupuesto para completar la instalación, ni tampoco para implantar el centro de control de la red sísmica digital por satélite que se quiere configurar en el país. El presente trabajo propone un diseño completo de la Red Sísmica Digital por Satélite Haitiana y la planificación para su implantación real, incluyendo las estaciones y el centro de control. Por ello se han estudiado las redes sísmicas modernas, las características de las redes sísmicas del Caribe, el sistema de transmisión Libra de Nanometrics y los software de adquisición y procesamiento de datos sísmicos Apollo y SeisComp3. También se ha estudiado la distribución espacial de las estaciones sísmicas con transmisión vía satélite instaladas en el país proponiendo alternativas y recomendaciones para futura ampliación, considerando los aspectos científicos, políticos y económicos, a la Isla de Vaca (Ile‐à‐Vache) en el extremo sur del país y la Isla de la Tortuga (Ile de la Tortue) al norte de la Falla Septentrional en el extremo norte del territorio haitiano.

Elimination of variability sources in NIR spectra for the determination of fat content in olive fruits

Models for prediction of oil content as percentage of dried weight in olive fruits were comput- ed through PLS regression on NIR spectra. Spectral preprocessing was carried out by apply- ing multiplicative signal correction (MSC), Sa vitzky–Golay algorithm, standard normal variate correction (SNV), and detrending (D) to NIR spectra. MSC was the preprocessing technique showing the best performance. Further reduction of variability was performed by applying the Wold method of orthogonal signal correction (OSC). The calibration model achieved a R 2 of 0.93, a SEPc of 1.42, and a RPD of 3.8. The R 2 obtained with the validation set remained 0.93, and the SEPc was 1.41.

Hyperspectral to multispectral imaging for detection of tree nuts and peanut traces in wheat flour

In current industrial environments there is an increasing need for practical and inexpensive quality control systems to detect the foreign food materials in powder food processing lines. This demand is especially important for the detection of product adulteration with traces of highly allergenic products, such as peanuts and tree nuts. Manufacturing industries dealing with the processing of multiple powder food products present a substantial risk for the contamination of powder foods with traces of tree nuts and other adulterants, which might result in unintentional ingestion of nuts by the sensitised population. Hence, the need for an in-line system to detect nut traces at the early stages of food manufacturing is of crucial importance. In this present work, a feasibility study of a spectral index for revealing adulteration of tree nut and peanut traces in wheat flour samples with hyperspectral images is reported. The main nuts responsible for allergenic reactions considered in this work were peanut, hazelnut and walnut. Enhanced contrast between nuts and wheat flour was obtained after the application of the index. Furthermore, the segmentation of these images by selecting different thresholds for different nut and flour mixtures allowed the identification of nut traces in the samples. Pixels identified as nuts were counted and compared with the actual percentage of peanut adulteration. As a result, the multispectral system was able to detect and provide good visualisation of tree nut and peanut trace levels down to 0.01% by weight. In this context, multispectral imaging could operate in conjuction with chemical procedures, such as Real Time Polymerase Chain Reaction and Enzyme-Linked Immunosorbent Assay to save time, money and skilled labour on product quality control. This approach could enable not only a few selected samples to be assessed but also to extensively incorporate quality control surveyance on product processing lines.

Detection and quantification of peanut traces in wheat flour by near infrared hyperspectral imaging spectroscopy using principal-component analysis

The use of a common environment for processing different powder foods in the industry has increased the risk of finding peanut traces in powder foods. The analytical methods commonly used for detection of peanut such as enzyme-linked immunosorbent assay (ELISA) and real-time polymerase chain reaction (RT-PCR) represent high specificity and sensitivity but are destructive and time-consuming, and require highly skilled experimenters. The feasibility of NIR hyperspectral imaging (HSI) is studied for the detection of peanut traces down to 0.01% by weight. A principal-component analysis (PCA) was carried out on a dataset of peanut and flour spectra. The obtained loadings were applied to the HSI images of adulterated wheat flour samples with peanut traces. As a result, HSI images were reduced to score images with enhanced contrast between peanut and flour particles. Finally, a threshold was fixed in score images to obtain a binary classification image, and the percentage of peanut adulteration was compared with the percentage of pixels identified as peanut particles. This study allowed the detection of traces of peanut down to 0.01% and quantification of peanut adulteration from 10% to 0.1% with a coefficient of determination (r2) of 0.946. These results show the feasibility of using HSI systems for the detection of peanut traces in conjunction with chemical procedures, such as RT-PCR and ELISA to facilitate enhanced quality-control surveillance on food-product processing lines.

Hyperspectral to multispectral imaging for detection of tree nuts and peanut traces in wheat flour.

In current industrial environments there is an increasing need for practical and inexpensive quality control systems to detect the foreign food materials in powder food processing lines. This demand is especially important for the detection of product adulteration with traces of highly allergenic products, such as peanuts and tree nuts. Manufacturing industries dealing with the processing of multiple powder food products present a substantial risk for the contamination of powder foods with traces of tree nuts and other adulterants, which might result in unintentional ingestion of nuts by the sensitised population. Hence, the need for an in-line system to detect nut traces at the early stages of food manufacturing is of crucial importance. In this present work, a feasibility study of a spectral index for revealing adulteration of tree nut and peanut traces in wheat flour samples with hyperspectral images is reported. The main nuts responsible for allergenic reactions considered in this work were peanut, hazelnut and walnut. Enhanced contrast between nuts and wheat flour was obtained after the application of the index. Furthermore, the segmentation of these images by selecting different thresholds for different nut and flour mixtures allowed the identification of nut traces in the samples. Pixels identified as nuts were counted and with the actual percentage of peanut adulteration. As a result, the multispectral system was able to detect and provide good visualisation of tree nut and peanut trace levels down to 0.01% by weight. In this context, multispectral imaging could operate in conjuction with chemical procedures, such as Real Time Polymerase Chain Reaction and Enzyme-Linked Immunosorbent Assay to save time, money and skilled labour on product quality control. This approach could enable not only a few selected samples to be assessed but also to extensively incorporate quality control surveyance on product processing lines.

VIS/NIR spectral signature for the identification of peanut contamination of powder foods

Visible-near infrared reflectance spectra are proposed for the characterization of IRMM 481 peanuts variety in comparison to powder food materials: wheat flour, milk and cocoa. Multidimensional analysis of reflectance spectra of powder samples shows a specific NIR band centred at 1200 nm that identifies peanut compared to the rest of food ingredients, regardless compaction level and temperature. Spectral range of 400-1000 nm is not robust for identification of blanched peanut. The visible range has shown to be reliable for the identification of pre-treatment and processing of unknown commercial peanut samples. A spectral index is proposed based on the combination of three wavelengths around 1200 nm that is 100% robust against pre-treatment (raw or blanched) and roasting (various temperatures and treatment duration).

Application of Independent Components Analysis with the JADE algorithm and NIR hyperspectral imaging for revealing food adulteration

In recent years, Independent Components Analysis (ICA) has proven itself to be a powerful signal-processing technique for solving the Blind-Source Separation (BSS) problems in different scientific domains. In the present work, an application of ICA for processing NIR hyperspectral images to detect traces of peanut in wheat flour is presented. Processing was performed without a priori knowledge of the chemical composition of the two food materials. The aim was to extract the source signals of the different chemical components from the initial data set and to use them in order to determine the distribution of peanut traces in the hyperspectral images. To determine the optimal number of independent component to be extracted, the Random ICA by blocks method was used. This method is based on the repeated calculation of several models using an increasing number of independent components after randomly segmenting the matrix data into two blocks and then calculating the correlations between the signals extracted from the two blocks. The extracted ICA signals were interpreted and their ability to classify peanut and wheat flour was studied. Finally, all the extracted ICs were used to construct a single synthetic signal that could be used directly with the hyperspectral images to enhance the contrast between the peanut and the wheat flours in a real multi-use industrial environment. Furthermore, feature extraction methods (connected components labelling algorithm followed by flood fill method to extract object contours) were applied in order to target the spatial location of the presence of peanut traces. A good visualization of the distributions of peanut traces was thus obtained