Spectroscopic techniques applied to the real time monitoring and control of the production processes of the pharmaceutical and food industry

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Chemometric Differentiation of Sole and Plaice Fish Fillets Using Three Near-Infrared Instruments

Fish species substitution is one of the most common forms of fraud all over the world, as fish identification can be very challenging for both consumers and experienced inspectors in the case of fish sold as fillets. The difficulties in distinguishing among different species may generate a “grey area” in which mislabelling can occur. Thus, the development of fast and reliable tools able to detect such frauds in the field is of crucial importance. In this study, we focused on the distinction between two flatfish species largely available on the market, namely the Guinean sole (Synaptura cadenati) and European plaice (Pleuronectes platessa), which are very similar looking. Fifty fillets of each species were analysed using three near-infrared (NIR) instruments: the handheld SCiO (Consumer Physics), the portable MicroNIR (VIAVI), and the benchtop MPA (Bruker). PLS-DA classification models were built using the spectral datasets, and all three instruments provided very good results, showing high accuracy: 94.1% for the SCiO and MicroNIR portable instruments, and 90.1% for the MPA benchtop spectrometer. The good classification results of the approach combining NIR spectroscopy, and simple chemometric classification methods suggest great applicability directly in the context of real-world marketplaces, as well as in official control plans

“Real time” quality control of incoming raw materials and conformity assessment of semi-finished and/or finished dietary supplement products.

The main task to accomplish for industrial organizations taking into account the costs and the environmental impact is the expected final product quality. To this aim, an integrated monitoring and control approach, based on the principles of process analytical technology (PAT), can be employed starting from the quality control of incoming raw materials, through the active process control, to final products variability reduction [1]. Near infrared spectroscopy (NIRS), providing non-invasive testing and quick analytical responses, in combination with multivariate data analysis, has shown the potential for performing both untargeted quality control and targeted calibrations of quality attributes [2]. The present study is related to raw materials and was mainly focused on the identity confirmation and/or classification of botanical raw materials, as active constituents in dietary supplement formulations, due to their complex chemical composition. The pattern recognition and classification models developed using principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) showed the potential for further industrial implementation. Moreover, the quantitative prediction of active ingredient(s) in the semi-finished and final products was successfully accomplished by taking advantage of the previously developed linear regression models, obtained using partial least squares (PLS) modelling and the pre-processed NIR spectra. Quick analytical responses regarding incoming raw materials quality as well as real-time assessment of the conformity of semi-finished products can improve the management of resources (natural, human and financial) and allow to plan the manufacturing processes accordingly

Storage conditions of different edible seed oils: studying the effects of time and illumination during shelf-life by NIR and Visible spectroscopies.

Shelf-life storage conditions play a paramount role on the preservation of food quality [1]. Protection from temperature and light is of great importance to preserve the nutritional, sensory and organoleptic properties of food products [2]. In particular, foodstuff containing vegetable edible oils can be particularly susceptible to thermal- and photo-degradation if poorly stored, leading to a possible negative impact on the consumer’s expectations. In this perspective, the present study was focused on vegetable oils extracted from three different seeds: hemp, linseed and sunflower. The oils were freshly obtained from the corresponding seeds using an expeller (screw) press, put in 250 mL dark glass bottles and promptly stored under controlled conditions at 20°C. A shelf-life study was carried out over a period of 12 months, with the aim of simulating the everyday storage conditions of a common supermarket. During the considered period, the bottles were separated in two sets, which were exposed to different artificial light sources (neon and LED) but with the same light power and colour (6500K and 1500 lumen). Every second month, one bottle from each set was taken out from the storage warehouse and the oil directly analysed by means of near-infrared (NIR) and Visible (Vis) spectroscopy. Multivariate exploratory analysis tools such as Principal Component Analysis (PCA) and Multivariate Curve Resolution (MCR) highlighted that the different oil types can be easily distinguished by both NIR and Visible spectroscopy, and also that the effect of storage time can be detected. Colour intensity decreased over time for all the oil types, especially in the case of the hemp oil, which at the beginning was the most intensely coloured one but resulted also the most subjected to photodegradation of its pigments (mainly represented by chlorophylls). Moreover, differences between the type of illumination were observed, suggesting that neon lamps may prove more detrimental for the preservation of seed oils than the newer and more energy-efficient LED lamps

Chemometric differentiation of sole and plaice fish fillets using three near-infrared instruments

Fish species substitution is one of the most common frauds all over the world, as fish identification can be very challenging for both consumers and experienced inspectors in the cases of fish sold as fillets. Along the fishery production chain, one of the most vulnerable food chains [1], species replacement can often occur [2–4]: indeed, the difficulties in distinguishing among different species may generate a “grey area” in which mislabelling can occur. Thus, the development of fast and reliable tools able to detect such frauds in field is of crucial importance. In this study we focused on the distinction between two flatfish species largely available on the market, namely the Guinean sole (Synaptura cadenati) and European plaice (Pleuronectes platessa), which are very similar looking. Fifty fillets of each species were analysed using three near-infrared (NIR) instruments: the handheld SCiO (Consumer Physics), the portable MicroNIR (VIAVI), and the benchtop MPA (Bruker). Exploratory principal component analysis (PCA, [5]) models and classification partial least squares–discriminant analysis (PLS-DA, [6]) models were built using the spectral datasets, and all three instruments provided very good results, showing high accuracy in classification: 94.1 % for the SCiO and MicroNIR portable instruments, 90.1 % for the MPA benchtop spectrometer. The good classification results of the approach combining NIR spectroscopy, and simple chemometric classification methods suggest great applicability directly in the context of real-world marketplaces, as well as in official control plans

Evaluation of different pressing systems for extraction of edible seed oils using NIR Spectroscopy and multivariate data analysis

Edible oils can be considered a valuable component of a healthy diet, due to the high content of unsaturated fatty acids, the presence of important quality micronutrients and their antioxidant properties. However, these valuable parameters, which can be successfully predicted using Near Infrared (NIR) Spectroscopy [1], may be highly affected by the method employed for oil extraction, resulting in different product yields and quality [2]. In this study, NIR spectroscopy coupled to multivariate data analysis was applied as a rapid and non-invasive method to compare edible seed oils extracted using two different kinds of presses, i.e. screw and hydraulic. For both the pressing systems, five kinds of vegetable seeds (hemp, linseed, sunflower, pumpkin, walnut) and two working conditions, related to degree of applied pressure and pressure time, were considered. A whole dataset of 336 NIR spectra was collected on the extracted oil samples in transmittance mode using a FT-NIR benchtop spectrometer (MPA, Bruker Optics). After an initial pre-processing of the NIR spectra using Standard Normal Variate (SNV) algorithm, Principal Component Analysis (PCA) models were built to get an overview of the samples’ distribution in the multivariate space. As a result, the oil samples grouped in well-defined clusters it the PCA score space, according to the type of seed. The PCA models, separately developed for each type of seed, highlighted that both the walnut and the linseed oils samples grouped in well-defined clusters according to the pressing system, suggesting a great effect of this operating parameter on the chemical composition of the extracted oils. Conversely, this effect was not found for the sunflower, pumpkin and hemp oils. Effects related to the degree of applied pressure and pressure time were not observed in any type of seed oils considered