Prediction of sensory attributes of European Emmental cheese using near-infrared spectroscopy: A feasibility study

Use of front face fluorescence spectroscopy coupled with multivariate data analysis for monitoring biscuits' quality during aging

In this study, the potentiality of front face fluorescence spectroscopy (FFFS) for the evaluation of the quality of biscuits manufactured with butylated hydroxytoluene and pomegranate peel extract during aging was investigated. By using the principal component analysis, vitamin A and tryptophan spectra allowed a clear discrimination between biscuit samples according to the nature of antioxidants, while fluorescent Maillard reaction products spectra showed clear differentiation between samples according to the storage time. Clear differentiation between biscuits according to the used antioxidants and storage time was achieved by using common components and specific weights analysis. Using partial least-squares regression, excellent prediction of water activity (R 2 = 0.95), and L* values (R 2 = 0.92), and approximate prediction of hardness (R 2 = 0.78), b* values (R 2 = 0.74), and moisture content (R 2 = 0.74) were shown. However, the FFFS failed to predict a* values, primary and secondary oxidation products (R 2 < 0.6).

From consumers' science to food functionality-Challenges and opportunities for vibrational spectroscopy

Current available methods used to measure or estimate the composition, functionality, and sensory properties of foods and food ingredients are destructive and time consuming. Therefore, new approaches are required by both the food industry and R&D organizations. Recent years have witnessed a steady growth on the applications and utilization of vibrational spectroscopy techniques [near (NIR), mid infrared (MIR), Raman] to analyse or estimate several properties in a wide range of foods and food ingredients. This chapter will provide with an overview of vibrational spectroscopy techniques, the combination of these techniques with multivariate data analysis, and examples on the use of these techniques to measure composition, and functional properties in a wide range of foods.

Preliminary study on the potential application of Fourier-transform mid-infrared for the evaluation of overall quality and authenticity of Moroccan virgin olive oil

BACKGROUND

Olive oil provides a wide range of health-promoting compounds. The quality of olive oil is an even more complex concept as it is affected by several factors, such as variety, season, stage of maturation, extraction processing, and so on. The main objective of this study was to determine the potential of chemical and mid-infrared spectroscopy techniques to determine the quality and authenticity of virgin olive oil (VOO). For this, we studied 41 VOOs originating from five regions of Morocco (Fez/Meknes, Eastern, Northern, Beni-Mellal/Khenifra, and Marrakech/Safi) and produced using different agricultural and technological conditions during two successive crop seasons (2015-2016 and 2016-2017).

RESULTS

By applying principal component analysis and factorial discriminant analysis with leave-one-out validation to the mid-infrared spectroscopy, clear discrimination between VOO samples according to their geographic origin and variety was observed, with correct classification rates of 91.87% and 91.87% being observed respectively. The application of partial least-squares regression to mid-infrared and chemical data sets allowed excellent prediction of free acidity, peroxide value, k₂₇₀ , and chlorophyll level with R² of 0.99, 0.97, 0.98, and 0.93 respectively, and good prediction of k₂₃₂ (R² = 0.84).

CONCLUSION

The results demonstrate that mid-infrared spectroscopy coupled with chemometric tools could be used as a rapid screening tool for evaluating the overall quality and authenticity of VOO. © 2020 Society of Chemical Industry.

An in-depth study of cheese ripening by means of NIR hyperspectral imaging: Spatial mapping of dehydration, proteolysis and lipolysis

Cheese represents one of the most complex food matrices, for the high number of factors contributing to the chemical composition, and so its evaluation represents an important analytical challenge. The present study describes an innovative and non-destructive analytical approach, based on hyperspectral imaging in the near-infrared region (HSI-NIR) and multivariate pattern recognition, to study and monitor the extent - spatial and temporal - of biochemical phenomena responsible for cheese ripening. NIR spectral bands characterising dehydration, proteolysis and lipolysis were individuated and studied by exploiting a representative sample set of characteristic cheeses. The information obtained was employed to develop score maps based on principal component analysis (PCA), which permitted to monitor and visualise the ripening of Formaggetta, a commercial semi-hard cheese typical of Liguria, an Italian region, providing a deep understanding of the evolution of dehydration, proteolysis and lipolysis during the maturation period that precedes the placing on the market.

Exploring the relationship between potato components and Maillard reaction derivative harmful products using multivariate statistical analysis

The correlation between potato components and Maillard reaction derivative harmful products (MRDHPs) formation during heat-processing was assessed in nine commercial potato varieties in China. Principal component analysis (PCA) combined with canonical correlation analysis (CCA) approach was performed to explore their relationships. The variables contributing most to the PCA results were extracted for CCA, and the results indicated that several amino acids, including lysine, tryptophan, alanine, phenylalanine, aspartate, and glutamate, have significant impacts on acrylamide and β-carboline heterocyclic amine formation. Moreover, 5-O-caffeoylquinic acid, 3-O-caffeoylquinic acid, α-solanine, and α-chaconine were also important factors associated with acrylamide and β-carboline heterocyclic amine formation. Optimally using raw potato varieties based on the impacts of these factors can help control MRDHP formation during thermal processing. For the first time, such approach was applied, which may be a useful tool for discovering the correlation of food components and MRDHPs.

Determination of pesticide residual levels in strawberry (Fragaria) by near-infrared spectroscopy

BACKGROUND

In this study, an infrared-based prediction method was developed for easy, fast and non-destructive detection of pesticide residue levels measured by reference analysis in strawberry (Fragaria × ananassa Duch, cv. Albion) samples using near-infrared spectroscopy and demonstrating its potential alternative or complementary use instead of traditional pesticide determination methods. Strawberries of Albion variety, which were supplied directly from greenhouses, were used as the study material. A total of 60 batch sample groups, each consisting of eight strawberries, was formed, and each group was treated with a commercial pesticide at different concentrations (26.7% boscalid + 6.7% pyraclostrobin) and varying residual levels were obtained in strawberry batches. The strawberry samples with pesticide residuals were used both to collect near-infrared spectra and to determine reference pesticide levels, applying QuEChERS (quick, easy, cheap, rugged, safe) extraction, followed by liquid chromatographic-mass spectrometric analysis.

RESULTS AND CONCLUSION

Partial least squares regression (PLSR) models were developed for boscalid and pyraclostrobin active substances. During model development, the samples were randomly divided into two groups as calibration (n = 48) and validation (n = 12) sets. A calibration model was developed for each active substance, and then the models were validated using cross-validation and external sets. Performance evaluation of the PLSR models was evaluated based on the residual predictive deviation (RPD) of each model. An RPD of 2.28 was obtained for boscalid, while it was 2.31 for pyraclostrobin. These results indicate that the developed models have reasonable predictive power. © 2019 Society of Chemical Industry.

Potentiality of front-face fluorescence and mid-infrared spectroscopies coupled with partial least square regression to predict lipid oxidation in pound cakes during storage

The potentialities of front-face fluorescence (FFF) and mid-infrared (MIR) spectroscopies coupled with partial least square regression (PLSR) were compared to predict the lipid oxidation of pound cakes. The level of lipid oxidation in pound cakes determined using classical methods showed some changes. Similarly, the fluorescence emission (305-490 nm) and excitation (252-390 nm) spectra and MIR spectra scanned in the 4000-700 cm^-1 region showed some changes in pound cakes as a function of both storage time and the type of oil used in the formulation. The application of PLSR to the MIR spectra, provided excellent predictive results for free fatty acid (R² = 0.97) and peroxide values (R² = 0.87). Similar results were obtained from both tryptophan and MIR spectra for the prediction of TOTOX (R² > 0.86) demonstrating the efficiency of the MIR and FFF spectroscopies to qualify and quantify the level of lipid oxidation in pound cakes.

Investigations into the Performance of a Novel Pocket-Sized Near-Infrared Spectrometer for Cheese Analysis

The performance of a newly developed pocket-sized near-infrared (NIR) spectrometer was investigated by analysing 46 cheese samples for their water and fat content, and comparing results with a benchtop NIR device. Additionally, the automated data analysis of the pocket-sized spectrometer and its cloud-based data analysis software, designed for laypeople, was put to the test by comparing performances to a highly sophisticated multivariate data analysis software. All developed partial least squares regression (PLS-R) models yield a coefficient of determination (R²) of over 0.9, indicating high correlation between spectra and reference data for both spectrometers and all data analysis routes taken. In general, the analysis of grated cheese yields better results than whole pieces of cheese. Additionally, the ratios of performance to deviation (RPDs) and standard errors of prediction (SEPs) suggest that the performance of the pocket-sized spectrometer is comparable to the benchtop device. Small improvements are observable, when using sophisticated data analysis software, instead of automated tools.

Intelligent sensing sensory quality of Chinese rice wine using near infrared spectroscopy and nonlinear tools

The approach presented herein reports the application of near infrared (NIR) spectroscopy, in contrast with human sensory panel, as a tool for estimating Chinese rice wine quality; concretely, to achieve the prediction of the overall sensory scores assigned by the trained sensory panel. Back propagation artificial neural network (BPANN) combined with adaptive boosting (AdaBoost) algorithm, namely BP-AdaBoost, as a novel nonlinear algorithm, was proposed in modeling. First, the optimal spectra intervals were selected by synergy interval partial least square (Si-PLS). Then, BP-AdaBoost model based on the optimal spectra intervals was established, called Si-BP-AdaBoost model. These models were optimized by cross validation, and the performance of each final model was evaluated according to correlation coefficient (Rp) and root mean square error of prediction (RMSEP) in prediction set. Si-BP-AdaBoost showed excellent performance in comparison with other models. The best Si-BP-AdaBoost model was achieved with Rp=0.9180 and RMSEP=2.23 in the prediction set. It was concluded that NIR spectroscopy combined with Si-BP-AdaBoost was an appropriate method for the prediction of the sensory quality in Chinese rice wine.

Non-invasive sensing for food reassurance

The main food quality traits of interest using non-invasive sensing techniques are sensory characteristics, chemical composition, physicochemical properties, health-protecting properties, nutritional characteristics and safety. A wide range of non-invasive sensing techniques, from optical, acoustical, electrical, to nuclear magnetic, X-ray, biosensor, microwave and terahertz, are organized according to physical principle.

Methodology for modeling the disinfection efficiency of fresh-cut leafy vegetables wash water applied on peracetic acid combined with lactic acid

A methodology to i) assess the feasibility of water disinfection in fresh-cut leafy greens wash water and ii) to compare the disinfectant efficiency of water disinfectants was defined and applied for a combination of peracetic acid (PAA) and lactic acid (LA) and comparison with free chlorine was made. Standardized process water, a watery suspension of iceberg lettuce, was used for the experiments. First, the combination of PAA+LA was evaluated for water recycling. In this case disinfectant was added to standardized process water inoculated with Escherichia coli (E. coli) O157 (6logCFU/mL). Regression models were constructed based on the batch inactivation data and validated in industrial process water obtained from fresh-cut leafy green processing plants. The UV254(F) was the best indicator for PAA decay and as such for the E. coli O157 inactivation with PAA+LA. The disinfection efficiency of PAA+LA increased with decreasing pH. Furthermore, PAA+LA efficacy was assessed as a process water disinfectant to be used within the washing tank, using a dynamic washing process with continuous influx of E. coli O157 and organic matter in the washing tank. The process water contamination in the dynamic process was adequately estimated by the developed model that assumed that knowledge of the disinfectant residual was sufficient to estimate the microbial contamination, regardless the physicochemical load. Based on the obtained results, PAA+LA seems to be better suited than chlorine for disinfecting process wash water with a high organic load but a higher disinfectant residual is necessary due to the slower E. coli O157 inactivation kinetics when compared to chlorine.

Physicochemical quality and chemical safety of chlorine as a reconditioning agent and wash water disinfectant for fresh-cut lettuce washing

Chlorine was assessed as a reconditioning agent and wash water disinfectant in the fresh-cut produce industry. Artificial fresh-cut lettuce wash water, made from butterhead lettuce, was used for the experiments. In the reconditioning experiments, chlorine was added to artificial wash water inoculated with Escherichia coli O157 (6 log CFU/ml). Regression models were constructed based on the inactivation data and validated in actual wash water from leafy vegetable processing companies. The model that incorporated chlorine dose and chemical oxygen demand (COD) of the wash water accurately predicted inactivation. Listeria monocytogenes was more resistant to chlorine reconditioning in artificial wash water than Salmonella spp. and Escherichia coli O157. During the washing process with inoculated lettuce (4 log CFU/g), in the absence of chlorine, there was a rapid microbial buildup in the water that accumulated to 5.4 ± 0.4 log CFU/100 ml after 1 h. When maintaining a residual concentration of 1 mg/liter free chlorine, wash water contamination was maintained below 2.7, 2.5, and 2.5 log CFU/100 ml for tap water and artificial process water with COD values of 500 and 1,000 mg O2/liter, respectively. A model was developed to predict water contamination during the dynamic washing process. Only minor amounts of total trihalomethanes were formed in the water during reconditioning. Total trihalomethanes accumulated to larger amounts in the water during the wash water disinfection experiments and reached 124.5 ± 13.4 μg/liter after 1 h of execution of the washing process in water with a COD of 1,000 mg O2/liter. However, no total trihalomethanes were found on the fresh-cut lettuce after rinsing.

Monitoring changes in sponge cakes during aging by front face fluorescence spectroscopy and instrumental techniques

In the present study, sponge cakes, produced at the pilot scale, were monitored during aging (i.e., 1, 3, 6, 9, 16, and 20 days) by three different analytical techniques. For the texture analyzer, the hardness and elasticity of crumb cakes were found to significantly increase and decrease, respectively, throughout aging. Color parameters (L*, a*, and b*) showed only slight change throughout aging, and a high correlation (R(2) = 0.88) was observed between the whiteness and the yellowness. Tryptophan fluorescence spectra (excitation, 290 nm; emission, 305-490 nm) recorded on cakes exhibited three maxima located at 382, 435, and 467 nm that were attributed to maximum emission of tryptophan (382 nm) and fluorescent Maillard reaction products (435 and 467 nm). The principal component analysis (PCA) applied to the tryptophan spectra allowed a clear discrimination of cakes aged for 1, 3, and 6 days from those aged for 9, 16, and 20 days. Finally, canonical correlation analysis (CCA) performed on the textural and tryptophan fluorescence spectral data sets showed that the two groups of variables were highly correlated because the squared canonical coefficients for canonical variates were 0.99, indicating that cake texture determined at the macroscopic level by texture analyzer is a reflection of its structure at the molecular level determined by fluorescence spectroscopy.

Structural changes of milk components during acid-induced coagulation kinetics as studied by synchronous fluorescence and mid-infrared spectroscopy

Dynamic oscillatory experiments and front-face synchronous fluorescence spectroscopy and mid-infrared (mid-IR) spectroscopy have been used to investigate structure evolution, at the macroscopic and molecular levels, during milk acidification kinetics. The studies were performed using skim milk, at two different temperatures (30 °C and 40 °C), to which was added glucono-δ-lactone (GDL) to generate different structural changes in casein micelles and gels. Synchronous fluorescence spectra were recorded in the 250-500 nm excitation wavelength range using an offset of 80 nm between the excitation and emission monochromators for each system during the 300 min acidification kinetics. The change in the fluorescence intensity at 281 nm reflects the pH-induced physicochemical changes of casein micelles and, in particular, structural changes in the micelles in the pH range 5.5-5.0. Regarding mid-infrared spectroscopy, the region located between 1700 and 1500 cm(-1), corresponding to the amide I and II bands, and the 1500-900 cm(-1) region, called the fingerprint region, were considered for the characterization of milk coagulation kinetics. Changes in the absorbance at 1063 cm(-1) as a function of pH for kinetics recorded at 30 °C and 40 °C reflected pH-induced phosphate dissolution in the pH range 5.5-5.0. Compared to rheometry, which reveals microstructure changes only in the gel state, spectroscopic methods make it possible to monitor molecular structure changes in micelles throughout the acidification processes.

Chemometric models for the quantitative descriptive sensory analysis of Arabica coffee beverages using near infrared spectroscopy

Mathematical models based on chemometric analyses of the coffee beverage sensory data and NIR spectra of 51 Arabica roasted coffee samples were generated aiming to predict the scores of acidity, bitterness, flavour, cleanliness, body and overall quality of coffee beverage. Partial least squares (PLS) were used to construct the models. The ordered predictor selection (OPS) algorithm was applied to select the wavelengths for the regression model of each sensory attribute in order to take only significant regions into account. The regions of the spectrum defined as important for sensory quality were closely related to the NIR spectra of pure caffeine, trigonelline, 5-caffeoylquinic acid, cellulose, coffee lipids, sucrose and casein. The NIR analyses sustained that the relationship between the sensory characteristics of the beverage and the chemical composition of the roasted grain were as listed below: 1 - the lipids and proteins were closely related to the attribute body; 2 - the caffeine and chlorogenic acids were related to bitterness; 3 - the chlorogenic acids were related to acidity and flavour; 4 - the cleanliness and overall quality were related to caffeine, trigonelline, chlorogenic acid, polysaccharides, sucrose and protein.

Effects of added minerals (calcium, phosphate, and citrate) on the molecular structure of skim milk as investigated by mid-infrared and synchronous fluorescence spectroscopies coupled with chemometrics

Minerals play an important role in the structure and stability of casein micelles: minerals and caseins in milk are in dynamic equilibrium. Front-face synchronous fluorescence and mid-infrared spectra in combination with multivariate statistical analysis have been used to investigate, at a molecular level, the effects of added minerals (calcium, phosphate, or citrate) on mineral equilibria and casein micelle structure. Synchronous fluorescence spectra were recorded in the 250-500 nm excitation wavelength range using an offset of 80 nm between the excitation and emission monochromators for skim-milk samples fortified with 0, 3, 6, and 9 mM of calcium, phosphate, or citrate at 30 degrees C and 4 degrees C. Regarding midinfrared spectroscopy, the region located between 1700-1500 cm(-1), corresponding to the amide I and II bands, and the 1500-900 cm(-1) region, called the fingerprint region, were considered for the characterization of the fortified skim-milk samples at the two considered temperatures. Principal component analysis (PCA) was applied to the collections of fluorescence and infrared spectral data of the two systems to optimize their description. The results show that the phenomena induced by the addition of phosphate were different from the ones observed following the addition of calcium or citrate, a calcium-chelating agent. Finally, common components and specific weights analysis was applied to infrared spectra and fluorescence data collected on fortified skim-milk samples. This analysis enabled the relationship between the different data tables to be established.

Effects of mild heating and acidification on the molecular structure of milk components as investigated by synchronous front-face fluorescence spectroscopy coupled with parallel factor analysis

This paper reports the potential of synchronous front-face fluorescence spectroscopy in the characterization at the molecular level of milk changes during mild heating from 4 to 50 degrees C and acidification in the pH range of 6.8 to 5.1. Synchronous fluorescence spectra were collected in the 250-550 nm excitation wavelength range using offsets of 20, 40, 60, 80, 100, 120, 140, 160, 180, 200, and 240 nm between excitation and emission monochromators. The potential of parallel factor (PARAFAC) analysis in the decomposition of the whole synchronous fluorescence data set into the contribution of each of the fluorescent compounds present in milk has been investigated for heating and acidification data sets. Models were fitted from 1 to 7 components. Considering the core consistency values, PARAFAC models with three components have been considered. The first three components explained 94.43% and 94.13% of the total variance for heating and acidification data sets, respectively. The loading profiles of the first and second components derived from PARAFAC analysis performed on heating and acidification data sets corresponded quite well with the characteristics of tryptophan and vitamin A fluorescence spectra, respectively. The third component corresponded to the riboflavin fluorescence spectrum. Considering the heating experiment, the profile of the concentration mode for the second component showed large variations according to the temperature, which were assigned to the melting of triglycerides between 4 and 50 degrees C. For the acidification experiment, drastic changes in the concentration modes of the three components were observed for pH below 5.6, in agreement with structural changes in casein micelles.

Relationships between sensory and physicochemical measurements in meat of rabbit from three different breeding systems using canonical correlation analysis

Meat from rabbits reared either according to a standard (STAND) or a high quality norm (LABEL) or a low growth breeding (RUSSE) system were submitted to a sensory evaluation and to a large set of physicochemical measurements (weight of retail cuts, colour parameters, ultimate pH, femur flexure test, Warner-Bratzler shear test, water holding capacities and cooking losses). STAND rabbit meat exhibited the most juicy meat in back and in leg (p<0.01). Leg tenderness significantly decreased (p<0.001) in the rank order STAND>LABEL>RUSSE. Canonical correlation analysis showed strong correlations between physicochemical and sensory variables (R(2)=0.73 and 0.68 between the two first pairs of canonical variates). Especially, sensory tenderness and WB shear test variables assessed on raw longissimus muscle (LL) were correlated. Fibrous attribute in back was correlated with cooking loss in LL. When analysed separately only RUSSE rabbits exhibited the same relations between variables as those calculated in whole dataset.