Data Fusion for Food Authentication: Fresh/Frozen–Thawed Discrimination in West African Goatfish (Pseudupeneus prayensis) Fillets

Ottavian, Matteo; Fasolato, Luca; Serva, Lorenzo; Facco, Pierantonio; Barolo, Massimiliano

doi:10.1007/s11947-013-1157-x

Cited by 44 publications

(27 citation statements)

References 35 publications

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“…The number of the latent variable used to perform the PLS was chosen by minimising the Root Mean Square Error in Cross-Validation (RMSE cv ). Variable importance in projection (VIP) index was calculated to underlie the most critical parameters in the regression (Ottavian et al 2014).…”

Section: Regression Algorithmsmentioning

confidence: 99%

Use of near-infrared spectroscopy and multivariate approach for estimating silage fermentation quality from freshly harvested maize

Serva

Marchesini

Chinello

et al. 2021

Italian Journal of Animal Science

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The study aimed to evaluate the most predictive traits of fresh maize and the most appropriate multivariate approach for estimating silage fermentation quality. The use of near infrared (NIRs) instruments allowed rapid, accurate and cheap analysis. Samples of fresh maize plant (n ¼ 822) from hybrids (Class Cultivar) of early and late classes, were harvested at three maturity stages: early, medium and late, in three areas (level input field) of 'low', 'medium' and 'high' soil fertility, along three consecutive years. Several algorithms of feature selection, regression, classification and machine learning, were tested. Maize silage fermentative quality was summarised through a Fermentative Quality Index (FQI). We found the most predictive traits as dry matter (DM), starch, and acid detergent lignin (ADL), with negative coefficients, or water-soluble carbohydrates (WSC) with a positive coefficient. FQI was significantly (p < 0.0001) affected by year (negatively for 2018), level input field (positively for high level) and maturity stage (negatively for the late harvest). The most satisfying results were attained using a stepwise regression algorithm (R 2 ¼ 0.48), improved by the introduction of fixed effects (R 2 ¼ 0.55) and partial least square discriminant analysis (PLS-DA), which was assessed through the Mattew Correlation Coefficient (MCC) in validation (MCC ¼ 0.57). Concluding, among the tested approaches, the use of linear regression after stepwise algorithm or the use of PLS could be of practical help for the farmers to the effective management of the ensiling process of maize plants, even though environmental conditions should be considered to improve the predictions. HIGHLIGHTSThe prediction of FQ at harvest would allow the farmer to tune up the ensiling process The prediction of FQ through the use of portable NIRs instruments was successful DM, starch and ADL were negatively related to FQ index ARTICLE HISTORY

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Section: Regression Algorithmsmentioning

confidence: 99%

Use of near-infrared spectroscopy and multivariate approach for estimating silage fermentation quality from freshly harvested maize

Serva

Marchesini

Chinello

et al. 2021

Italian Journal of Animal Science

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“…The 1400–1500 nm wavelength range is related to the combination of O–H stretching and bending bands [ 37 ] and can be essentially linked to an increase of free water species during thawing [ 21 , 23 , 24 ].…”

Section: Discussionmentioning

confidence: 99%

“…Nowadays NIR spectroscopy is a well-established analytical technique, which has been already applied in quality control laboratories for monitoring the composition of intact samples (targeted analysis) of cereals, milk, and meat [ 16 , 17 , 18 ]. Moreover, NIR studies concerning both the freshness assessment of fish [ 15 , 19 , 20 ] and the fish authentication were reported, by also considering different species [ 21 , 22 , 23 ]. Surprisingly, to the best of authors’ knowledge, only few studies are available for rapid discrimination of fresh/thawed cephalopods by using NIR spectroscopy [ 24 ], even if they are a class of seafood highly exposed to fraudulent practices.…”

Section: Introductionmentioning

confidence: 99%

Differentiation between Fresh and Thawed Cephalopods Using NIR Spectroscopy and Multivariate Data Analysis

Pennisi

Giraudo

Cavallini

et al. 2021

Foods

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The sale of frozen–thawed fish and fish products, labeled as fresh, is currently one of the most common and insidious commercial food frauds. For this reason, the demand of reliable tools to identify the storage conditions is increasing. The present study was performed on two species, commonly sold in large-scale distribution: Cuttlefish (Sepia officinalis) and musky octopus (Eledonespp.). Fifty fresh cephalopod specimens were analyzed at refrigeration temperature (2 ± 2 °C), then frozen at −20 °C for 10 days and finally thawed and analyzed again. The performance of three near-infrared (NIR) instruments in identifying storage conditions were compared: The benchtop NIR Multi Purpose Analyzer (MPA) by Bruker, the portable MicroNIR by VIAVI and the handheld NIR SCiO by Consumer Physics. All collected spectra were processed and analyzed with chemometric methods. The SCiO data were also analyzed using the analytical tools available in the online application provided by the manufacturer to evaluate its performance. NIR spectroscopy, coupled with chemometrics, allowed discriminating between fresh and thawed samples with high accuracy: Cuttlefish between 82.3–94.1%, musky octopus between 91.2–97.1%, global model between 86.8–95.6%. Results show how food frauds could be detected directly in the marketplace, through small, ultra-fast and simplified handheld devices, whereas official control laboratories could use benchtop analytical instruments, coupled with chemometric approaches, to develop accurate and validated methods, suitable for regulatory purposes.

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“…Pre-treated dataset struments (i.e. portable Vis/NIR spectrometer, digital camera and texture analyzer) for the discrimination of fresh and frozen-thawed West African goatfish (classification accuracy of 100%) (Ottavian et al, 2014). As regards cod and haddock fillet authentication, Grassi et al (2018) proposed a near infrared spectroscopy approach able to correctly discriminate 100% of fillets.…”

Section: Untreated Datasetmentioning

confidence: 99%

Fish fillet authentication by image analysis

Grassi

Casiraghi

Alamprese

2018

Journal of Food Engineering

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The work aims at developing an image analysis procedure able to distinguish high value fillets of Atlantic cod (Gadus morhua) from those of haddock (Melanogrammus aeglefinus). The images of fresh G. morhua (n = 90) and M. aeglefinus (n = 91) fillets were collected by a flatbed scanner and processed at different levels. Both untreated and edge-based segmented (Canny algorithm) regions of interest were submitted to surface texture evaluation by Grey Level Co-occurrence Matrix analysis. Twelve surface texture variables selected by Principal Component Analysis or by SELECT algorithm were then used to develop Linear Discriminant Analysis models. An average correct classification rate ranging from 86.05 to 92.31% was obtained in prediction, irrespective the use of raw or segmented images. These findings pave the way for a simple machine vision system to be implemented along fish market chain, in order to provide stakeholders with a simple, rapid and cost-effective system useful in fighting commercial frauds.

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Data Fusion for Food Authentication: Fresh/Frozen–Thawed Discrimination in West African Goatfish (Pseudupeneus prayensis) Fillets

Cited by 44 publications

References 35 publications

Use of near-infrared spectroscopy and multivariate approach for estimating silage fermentation quality from freshly harvested maize

Use of near-infrared spectroscopy and multivariate approach for estimating silage fermentation quality from freshly harvested maize

Differentiation between Fresh and Thawed Cephalopods Using NIR Spectroscopy and Multivariate Data Analysis

Fish fillet authentication by image analysis

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