The use of statistical software in food science and technology: Advantages, limitations and misuses

Effect of Bacterial Endophytes Isolated from Tropical Fruits against Listeria monocytogenes and Cronobacter sakazakii in Model Food Products

Listeria monocytogenes and Cronobacter sakazakii are two important foodborne bacterial pathogens. Bacterial endophytes, which reside in plant cells, can produce antimicrobial compounds to protect the host organism or inhibit pathogens. This study investigated the bacterial community of tropical fruits for their potential to inactivate L. monocytogenes or C. sakazakii in cantaloupe and liquid infant formula, respectively. Tropical fruits including papayas, dragon fruits, and sugar apples, were sourced from several countries. Candidate bacterial endophytes were recovered from these tropical fruits using blood agar and Reasoner's 2A (R2A) agar and tested for potential inhibition against L. monocytogenes and C. sakazakii. A total of 196 bacterial endophytes were recovered from papayas, dragon fruits, and sugar apples. Among these bacterial endophytes, 33 (16.8%) and 13 (6.6%) of them demonstrated an inhibition zone against L. monocytogenes and C. sakazakii, respectively. The inhibitory strains were identified using 16S rRNA sequencing as Bacillus spp., Enterobacter spp., Klebsiella spp., Microbacterium spp., Pantoea spp., and Pseudomonas spp. A cocktail of Pantoea spp. and Enterobacter spp. was used in challenge studies with cantaloupe and significantly reduced the number of L. monocytogenes by approximately 2.5 log10 CFU/g. In addition, P. stewartii demonstrated antagonistic activity against C. sakazakii in liquid infant formula, i.e., it significantly decreased the number of C. sakazakii by at least 1 log10 CFU/mL. Thus, the use of bacterial endophytes recovered from fruits and vegetables could be a promising area of research. Their use as potential biocontrol agents to control bacterial pathogens in ready-to-eat foods warrants further investigation.

Multivariate Statistical Analysis for the Classification of Sausages Based on Physicochemical Attributes, Using Attenuated Total Reflectance-Fourier Transform Infrared (ATR-FTIR) and Inductively Coupled Plasma-Mass Spectrometry (ICP-MS)

Sausage is a convenient food that is widely consumed in the world and in Vietnam. Due to the rapid development of this product, the authenticity of many famous brands has faded by the rise of adulteration. Therefore, in this study, principal component analysis (PCA) was combined with chemical analysis to identify 6 sausage brands. Sausage samples were dried and then ground to a fine powder for both instrumental analyses of attenuated total reflectance-Fourier transform infrared (ATR-FTIR) and inductively coupled plasma-mass spectrometry (ICP-MS). Dried measurements of ATR-FTIR was performed directly on the ZnSe crystal, while elemental data were obtained through microwave digestion before the ICP-MS analysis. Principal component analysis (PCA) within the framework software of XLSTAT and STATISTICA 12 was performed on the spectroscopy and elemental dataset of sausage samples. PCA visualized the distinction of 6 sausage brands on both datasets of ATR-FTIR and ICP-MS. The classification on the spectroscopy profile showed that although more than 90% variation of the dataset was explained on the first two PCs, the difference between several brands was not detected as the distribution of data overlapped with one another. The PCA observation of the elemental composition on PC1 and PC3 has separated the sausage brands into 6 distinctive groups. Besides, several key elements contributed to the brands' identification have been detected, and the most distinctive elements are Na, K, Ca, and Ba. PCA visualization showed the feasibility of the classification of sausage samples from different brands when combined with the results of FT-IR and ICP-MS methods. The experiment was able to differentiate the sausages from the 5 brands using multivariate statistics.

Singular Olive Oils from a Recently Discovered Spanish North-Western Cultivar: An Exhaustive 3-Year Study of Their Chemical Composition and In-Vitro Antidiabetic Potential

In this work, the quality and physicochemical parameters, phenolic composition, and antidiabetic potential of olive oils obtained from olives belonging to centenarian olive trees of the so-called ‘Mansa de Figueiredo’ cultivar were evaluated during three consecutive crop seasons (2017–2019). The oils produced during the three crop years were classified as extra virgin based on the quality-related indices, sensory analysis, and the genuineness-related parameters. In addition, LC-ESI-TOF MS was used to get a comprehensive characterisation of the phenolic fraction while LC-ESI-IT MS was applied for quantitation purposes. The content of phenolic compounds (ranging from 1837 to 2434 mg/kg) was significantly affected by the harvest year due to the environmental conditions and ripening index. Furthermore, although significant differences in the inhibitory effects against the α-glucosidase enzyme for the EVOOs extracted throughout the three successive years were detected, all the studied EVOOs exhibited a stronger inhibitor effect than that found for acarbose.

Anthocyanins and Carotenoids Characterization in Flowers and Leaves of Cyclamen Genotypes Linked with Bioactivities Using Multivariate Analysis Techniques

The present study was carried out to evaluate and compare in vitro antioxidant (2,2-diphenyl-1-picrylhydrazyl (DPPH), Trolox equivalent antioxidant capacity (TEAC), and ferric reducing antioxidant power (FRAP)), antimicrobial, anticancer activities, and the individual carotenoids and anthocyanins content of methanol extracts of the Cyclamen genotypes: Persian cyclamen accessions (Cyclamen persicum Mill.), sowbread (C. mirabile Hildebr.), and ivy-leaved cyclamen (C. hederifolium Mill.) aerial parts. The HPLC-PDA analysis revealed the presence of five individual carotenoids (i.e., neoxanthin, violaxanthin, lutein, β-carotene, and cis-β-carotene) as the main compounds in Cyclamen leaves, and the presence of seven individual anthocycanins (i.e., cyanidin 3,5-di-O-glucoside, peonidin-rutinoside, peonidin 3,5-di-O-glucoside, peonidin 3-O-glucoside, malvidin 3-O-glucoside, malvidin 3,5-di-O-glucoside, and malvidin-rutinoside) in Cyclamen flowers reported, hereby, for the first time. The highest phenolic content was found in the leaves of LC6, C. mirabile (46.32 ± 0.14 mg/g gallic acid equivalents [GAE]), and in the flowers of C. persicum Merengue Magenta (FC15) (58.63 ± 0.17 mg/g GAE), whereas the highest flavonoid content was reported in C. persicum Halios Falbala leaves, namely LC9 (54.90 ± 0.27 mg/g quercetin equivalents [QE]) and in flowers of C. persicum Victora (FC2) (77.87 ± 0.25 mg/g QE). The highest antioxidant activity in DPPH and FRAP assays was reported in C. persicum Dark Violet (LC1) and Victoria (LC2), whereas C. mirabile (LC6) had the highest activity in the TEAC assay. In flowers, high antioxidant activities in DPPH and TEAC were noticed in C. persicum Superserie Red (FC7) and Dark Violet (FC1), respectively, and Halios Falbala (FC9) exhibited the highest activity in the TEAC assay. Additionally, FC9 exhibited the highest antibacterial activity in almost all tested bacteria compared with the leaves extracts. Furthermore, the highest in vitro citotoxicity in MDA-MB-231 cells was noticed in C. hederifolium LC18 (56.71-69.35%) and FC18 (40.07-41.43%), with a lower effect against BJ cells demonstrating selective toxicity. The above findings, highlight the potential use of the Cyclamen flower and leaf extracts as significant anticancer agents along with their antioxidant and antimicrobial properties.

NMR in Metabolomics: From Conventional Statistics to Machine Learning and Neural Network Approaches

NMR measurements combined with chemometrics allow achieving a great amount of information for the identification of potential biomarkers responsible for a precise metabolic pathway. These kinds of data are useful in different fields, ranging from food to biomedical fields, including health science. The investigation of the whole set of metabolites in a sample, representing its fingerprint in the considered condition, is known as metabolomics and may take advantage of different statistical tools. The new frontier is to adopt self-learning techniques to enhance clustering or classification actions that can improve the predictive power over large amounts of data. Although machine learning is already employed in metabolomics, deep learning and artificial neural networks approaches were only recently successfully applied. In this work, we give an overview of the statistical approaches underlying the wide range of opportunities that machine learning and neural networks allow to perform with accurate metabolites assignment and quantification.Various actual challenges are discussed, such as proper metabolomics, deep learning architectures and model accuracy.

Distinguishing aroma profile of highly-marbled beef according to quality grade using electronic nose sensors data and chemometrics approach

Fat deposition in animal muscles differs according to the genetics and muscle anatomical locations. Moreover, different fat to lean muscle ratios (quality grade, QG) might contribute to aroma development in highly marbled beef. Scientific evidence is required to determine whether the abundance of aroma volatiles is positively correlated with the amount of fat in highly marbled beef. Therefore, this study aims to investigate the effect of QG on beef aroma profile using electronic nose data and a chemometric approach. An electronic nose with metal oxide semiconductors was used, and discrimination was performed using multivariate analysis, including principal component analysis and hierarchical clustering. The M. longissimus lumborum (striploin) of QG 1++, 1+, 1, and 2 of Hanwoo steers (n=6), finished under identical feeding systems on similar farms, were used. In contrast to the proportion of monounsaturated fatty acids (MUFAs), the abundance of volatile compounds and the proportion of polyunsaturated fatty acids (PUFAs) decreased as the QG increased. The aroma profile of striploin from carcasses of different QGs was well-discriminated. QG1++ was close to QG1+, while QG1 and QG2 were within a cluster. In conclusion, aroma development in beef is strongly influenced by fat deposition, particularly the fat-to-lean muscle ratio with regard to the proportion of PUFA. As MUFA slows down the oxidation and release of volatile compounds, leaner beef containing a higher proportion of PUFA produces more volatile compounds than beef with a higher amount of intramuscular fat.

Application of multivariate data analysis for food quality investigations: An example-based review

These days, large multivariate data sets are common in the food research area. This is not surprising as food quality, which is important for consumers, and its changes are the result of a complex interplay of multiple compounds and reactions. In order to comprehensively extract information from these data sets, proper data analysis tools should be applied. The application of multivariate data analysis (MVDA) is therefore highly recommended. However, at present the use of MVDA for food quality investigations is not yet fully explored. This paper focusses on a number of MVDA methods (PCA (Principal Component Analysis), PLS (Partial Least Squares Regression), PARAFAC (Parallel Factor Analysis) and ASCA (ANOVA Simultaneous Component Analysis)) useful for food quality investigations. The terminology, main steps and the theoretical basis of each method will be explained. As this is an example-based review, each method was applied on the same experimental data set to give the reader an idea about each selected MVDA method and to make a comparison between the outcomes. Numerous MVDA methods are available in literature. Which method to select depends on the data set and objective. PCA should be the first choice for data exploration of two-dimensional data. For predictive purposes, PLS is the most appropriate method. Given an underlying experimental design, ASCA takes into account both the relation between the different variables and the design factors. In case of a multi-way data set, PARAFAC can be used for data exploration. While these methods have already proven their value in research, there is a need to further explore their potential to investigate the complex interplay of compounds and reactions contributing to food quality. With this work we would like to encourage food scientists with no or limited knowledge of MVDA to get some first insights into the selected methods.

Insights on the role of chemometrics and vibrational spectroscopy in fruit metabolite analysis

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The use of vibrational spectroscopy combined with data analytics is discussed.

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The measure of bioactive compounds metabolites in fruit samples is presented.

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Advantages and limitations of these techniques are discussed.

The last three decades have demonstrated the ability of combining data analytics (e.g. big data, machine learning) with modern analytical instrumental techniques such as vibrational spectroscopy (VIBSPEC) (e.g. NIR, Raman, MIR) and sensing technologies (e.g. electronic noses and tongues, colorimetric sensors) to analyse, measure and monitor a wide range of properties and samples. Developments in instrumentation, hardware and software have placed VIBSPEC as a useful tool to quantify several bioactive compounds and metabolites in a wide range of fruit and plant samples. With the incorporation of hand-held and portable instrumentation, these techniques have been valuable for the development of in-field and high throughput applications, opened new frontiers of analysis in fruits and plants. This review will present and discuss some of the current applications on the use of VIBSPEC techniques combined with data analytics on the measurement bioactive compounds and plant metabolites in different fruit samples.

Statistical Analysis of Chemical Element Compositions in Food Science: Problems and Possibilities

In recent years, many analyses have been carried out to investigate the chemical components of food data. However, studies rarely consider the compositional pitfalls of such analyses. This is problematic as it may lead to arbitrary results when non-compositional statistical analysis is applied to compositional datasets. In this study, compositional data analysis (CoDa), which is widely used in other research fields, is compared with classical statistical analysis to demonstrate how the results vary depending on the approach and to show the best possible statistical analysis. For example, honey and saffron are highly susceptible to adulteration and imitation, so the determination of their chemical elements requires the best possible statistical analysis. Our study demonstrated how principle component analysis (PCA) and classification results are influenced by the pre-processing steps conducted on the raw data, and the replacement strategies for missing values and non-detects. Furthermore, it demonstrated the differences in results when compositional and non-compositional methods were applied. Our results suggested that the outcome of the log-ratio analysis provided better separation between the pure and adulterated data and allowed for easier interpretability of the results and a higher accuracy of classification. Similarly, it showed that classification with artificial neural networks (ANNs) works poorly if the CoDa pre-processing steps are left out. From these results, we advise the application of CoDa methods for analyses of the chemical elements of food and for the characterization and authentication of food products.

Enzymatic and ultrasonic-assisted pretreatment in the extraction of bioactive compounds from Monguba (Pachira aquatic Aubl) leaf, bark and seed

The present study aims to characterize leaf, bark and seed of monguba in terms of their physicochemical and bioactive composition, and to determine total phenolic compounds (TPC) and total flavonoids (TF), well as their antioxidant activities (AA), of three organic solvent extracts with and without enzyme pretreatment by ultrasonic assisted extraction. Physicochemical composition was measured by pH, total titratable acidity, total soluble solids, moisture, ashes, lipids, crude protein, raw fiber, total carbohydrates, and water activity as well as, phytochemical composition analysis constituted of sugars, condensed (CT) and hydrolysable tannins (HT), carotenoids, total anthocyanins (TA), and organic acids contents. TPC and TF contents, and UHPLC/PDA/QDa flavonoids and phenolic acids quantification were performed for the solvent extracts. Antioxidant activity was determined by radical scavenging capacity assays (ABTS, DPPH, and ORAC), and reducing power assay (FRAP). Results showed that the leaf stood out with higher concentrations of ash, HT, TA and carotenoids; the bark with higher concentrations of raw fiber, total carbohydrates and organic acids (tartaric, quinic and 3.4-dihydroxybenzoic acids); in contrast, the seeds showed high concentrations of lipids, crude protein, sugars (fructose and sucrose), CT, and high values in all AA. The solvents significantly influenced the extraction of TPC and TF, highlighting ethanol. In general, the enzymatic treatments empowered the phenolic extraction and AA. The monguba seed extracts showed higher concentrations of hydroxycinnamic acids (chlorogenic acid, mainly), and flavanols (catechin and epicatechin), whereas the leaf extracts, flavanones (narigenin), flavonols (rutin, mainly) and flavones (acacetin). The bark extracts stood out for the presence of vanillin. The monguba seed extract can be used in functional foods production.

Insights into chemical and sensorial aspects to understand and manage beer aging using chemometrics

Beer chemical instability remains, at present, the main challenge in maintaining beer quality. Although not fully understood, after decades of research, significant progress has been made in identifying "aging compounds," their origin, and formation pathways. However, as the nature of aging relies on beer manufacturing aspects such as raw materials, process variables, and storage conditions, the chemical profile differs among beers. Current research points to the impact of nonoxidative reactions on beer quality. The effect of Maillard and Maillard intermediates on the final beer quality has become the focus of beer aging research, as prevention of oxidation can only sustain beer quality to some extent. On the other hand, few studies have focused on tracing a profile of whose compound is sensory relevant to specific types of beer. In this matter, the incorporation of "chemometrics," a class of multivariate statistic procedures, has helped brewing scientists achieve specific correlations between the sensory profile and chemical data. The use of chemometrics as exploratory data analysis, discrimination techniques, and multivariate calibration techniques has made the qualitatively and quantitatively translation of sensory perception of aging into manageable chemical and analytical parameters. However, despite their vast potential, these techniques are rarely employed in beer aging studies. This review discusses the chemical and sensorial bases of beer aging. It focuses on how chemometrics can be used to their full potential, with future perspectives and research to be incorporated in the field, enabling a deeper and more specific understanding of the beer aging picture.

The Sample, the Spectra and the Maths-The Critical Pillars in the Development of Robust and Sound Applications of Vibrational Spectroscopy

The last two decades have witnessed an increasing interest in the use of the so-called rapid analytical methods or high throughput techniques. Most of these applications reported the use of vibrational spectroscopy methods (near infrared (NIR), mid infrared (MIR), and Raman) in a wide range of samples (e.g., food ingredients and natural products). In these applications, the analytical method is integrated with a wide range of multivariate data analysis (MVA) techniques (e.g., pattern recognition, modelling techniques, calibration, etc.) to develop the target application. The availability of modern and inexpensive instrumentation together with the access to easy to use software is determining a steady growth in the number of uses of these technologies. This paper underlines and briefly discusses the three critical pillars-the sample (e.g., sampling, variability, etc.), the spectra and the mathematics (e.g., algorithms, pre-processing, data interpretation, etc.)-that support the development and implementation of vibrational spectroscopy applications.

Descriptive sensory analysis and consumer preferences of bean sauces

Sensory acceptability of value-added bean products is a critical determinant of their consumption. This study determined the factors influencing consumer preference of processed beans. Dry common beans (Phaseolus vulgaris L) processed by boiling, roasting, and extrusion were milled into flour and used to make bean sauces. The sauces were evaluated by 10 panelists using quantitative descriptive analysis and ranked by 120 consumers for preference. The factors influencing consumer preference were computed by a partial least squares regression model. The results showed that sauces were more distinguishable by appearance, taste, and mouth-feel than by aroma, flavor, and after-taste. Sauces that were brown in color, with burnt aroma and burnt after-taste were preferred to those that were described as lumpy with mashed potato flavor. Oven roasted beans and boiled beans were preferred to traditionally roasted, extruded, and unprocessed beans. Preference was significantly (p < .05) positively influenced by brown color and negatively by lumpiness which were both sensory descriptors of the appearance of sauce. Appearance is therefore the leading influencer of consumer preference in bean sauces and should be prioritized by product developers over other sensory attributes in development of similar products for wider acceptance and utilization of common beans.

Development of sorghum-based shortbread biscuits from "muskwari" flour

In order to produce biscuits from off-season sorghum, a local "Muskwari" sorghum was milled and sieved. This flour was used to produce shortbread biscuits with different substitutions rates of wheat flour to that of sorghum. The standard formulation of this same type of shortbread biscuits was used and biscuits were produced with incorporation rates of wheat flour to that of sorghum, from 0% to 100%, with a gap of 10 between two consecutives percentages. The technological characterization of the sorghum flour produced indicates a good water absorption capacity, and interesting solubility index and swelling rate. Technological aspect indicated that by changing speed and kneading time, resting the dough, it is possible to produce 100% sorghum flour shortbread biscuits. Shortbread biscuits made from 70% of wheat flour incorporation had the best average scores for overall preference criteria (6.97 ± 1.30), color (7.1 ± 1.45), and texture (6.62 ± 1.54). For smell and taste criteria, the 40% biscuits and the witness received the highest average scores, respectively, namely 6.77 ± 1.55 for smell and 7.12 ± 1.29 for taste. Analysis of the nutritional and energy intake of the control biscuit and the 70% substitution revealed that between the two, the latter had a significantly higher intake of total carbohydrates (58.51 g), dietary fiber (2.15 g), and total energy (454.1 kcal.

Response surface optimization of phenolic compounds from jabuticaba (Myrciaria cauliflora [Mart.] O.Berg) seeds: Antioxidant, antimicrobial, antihyperglycemic, antihypertensive and cytotoxic assessments

The aim of this study was to evaluate the effects of different solvents and maximize the extraction of bioactive compounds from jabuticaba (Myrciaria cauliflora) seeds. In general, the solvent system composed of water and propanone (52:48 v/v) modified the extract polarity and increased extraction yield of bioactive compounds. The optimized extract presented antioxidant capacity measured by different chemical and biological assays. The optimized extract exerted antiproliferative and cytotoxic effects against A549 and HCT8 cells, antimicrobial and antihemolytic effects, inhibited α-amylase/α-glucosidase activities and presented in vitro antihypertensive effect. Nonetheless, the optimized extract showed no cytotoxicity in a human cell model (IMR90). Vescalagin, castalagin and ellagic acid were the major phenolic compounds in the optimized extract. Our results show that jabuticaba seed may be a potential ingredient for the development of potentially functional foods.

A Chemometric Approach to Establish Underlying Connections between Lipid and Protein Oxidation and Instrumental Color and Texture Characteristics in Brazilian Dry-cured Loin

This study aimed to use chemometrics to evaluate the influence of lipid and protein oxidation on the color and texture characteristics of Brazilian dry-cured loin (Socol, BDL). Upon exploration using hierarchical cluster analysis (HCA), two clusters were formed, indicating that higher water activity (aw) was associated with higher lipid and protein oxidation. However, this fact was associated with softening and low color quality (a*, chroma, and cured color). In a more in-depth exploration, using principal component analysis (PCA) for each cluster separately, connections between protein and lipid oxidation were found in high aw, as demonstrated by their statistical association. In the same way, relationships between high hardness and carbonyl contents were obtained only in high aw. In addition, an overall relationship (p < 0.05) between nondestructive measurements, such as hardness, and destructive methods (malonaldehyde and carbonyl contents) demonstrate that nondestructive techniques can be promising for further studies in the method replacement field. In this study, reasonable explanations of the connections between oxidative damage and quality traits in Socol are provided.

Analysis of chemical compounds in beverages - Guidance for establishing a compositional analysis

In recent years, much analysis has been carried out regarding the chemical compositions of nonalcoholic and alcoholic beverages. However, no study has ever considered the compositional pitfalls inherent in such studies, which may lead to arbitrary results. Two approaches were compared in this paper, compositional data analysis (CoDa) and classical statistical analyses, to demonstrate (1) how aging affects beer and (2) how the results vary depending on the applied approach. Exemplarily, chemical compounds of 43 beer samples were analyzed. Our work has led us to obtain different results when the correlation analysis and principal component analysis were conducted on the unmodified-, log-transformed-, or closed-data in contrary to the analyses of log-ratio coordinates. The outcome of the latter provided better separation between aged and fresh beer samples and also easier interpretability. These results lead us to advise the usage of CoDa methods for the analyses of chemical compositions of beverages.

On-line prediction of hazardous fungal contamination in stored maize by integrating Vis/NIR spectroscopy and computer vision

This work presents a fusion scheme to combine visible/near infrared spectroscopy and computer vision for on-line detection of Aspergillus spp. and Fusarium spp. contamination in stored maize. Spectroscopy and image information of 270 groups of maize kernels were collected at speed of 0.15 m/s. Principal component analysis indicated fungi growth on maize could be monitored by both techniques. Spectroscopy method was found sensitive for infection level identification, while computer vision was more effective for fungal strain recognition. Linear discriminant analysis based on fusion of spectral and image features provided 100% accuracy for discrimination of samples infected by different strains after stored for 12 d, which is at least 5.6% higher than single-type features. Classification rate of samples with different infection levels achieved 92.2%, also 5.5% and 10.0% higher than single technique. Moreover, data fusion improved colony counts prediction in samples by partial least squares regression, with root mean-square error of prediction value being reduced by 25.0% and 17.4%, respectively. This study demonstrated the superiority of data fusion for fungal detection in grain during on-line processing.

Structure-activity relationships study on biological activity of peptides as dipeptidyl peptidase IV inhibitors by chemometric modeling

The aim of this study is to identify the potential descriptors affecting the inhibitory activity of the peptides inhibiting dipeptidyl peptidase IV (DPP-IV). This study provides important information for assessing the biological activity of the new peptide sequences of food origin or making structural modifications to the current inhibitors to improve their performance. For this purpose, the chemometric method describing the relationship between the structure of food peptides and their biological activity (structure-activity relationship [SAR]) was used to theoretically predict the potential of bioactivity of peptides. Data on the physicochemical properties of amino acids in the dipeptides acting as inhibitors of DPP-IV were collected and analyzed for using these properties as descriptors in further analysis. A total of 252 dipeptide sequences with confirmed DPP-IV inhibitory activity available in the BIOPEP-UWM database were included in the analysis, and 16 descriptors defining individual amino acids (such as molecular weight, polarity, hydropathicity, bulkiness, buried residue, and acceptable and normalized frequency of alpha-helix and beta-sheet) were identified. Based on this information, a data matrix was constructed and used in the chemometric analysis (principal component analysis and multiple linear regression). From the SAR model created, a multiple regression equation was derived to predict the biological activity of the dipeptide DPP-IV inhibitors.

Comparison of Common Analytical Methods for the Quantification of Total Polyphenols and Flavanols in Fruit Juices and Ciders

Multiple analytical methods are used for quantification of total polyphenols and total flavanols in fruit juices and beverages. Four methods were evaluated in this study: Folin-Ciocalteu (F-C), Lowenthal permanganate (L-P), 4-dimethylaminocinnamaldehyde (DMAC), and the bovine serum albumin (BSA) precipitation method. Method validation parameters, including working range, limit of detection, limit of quantitation, precision (repeatability), accuracy, and specificity, were assessed and compared. The F-C method was not specific to polyphenols, and the L-P method had the widest working range but lacked accuracy. The DMAC method was the most specific to flavanols, and the BSA method was not suitable for quantification of smaller flavanols, such as catechin and epicatechin. Quantitative performance was evaluated using commercial fruit juice samples (n = 14), apple juice samples of different cultivars (n = 22), and commercial ciders (n = 17). In general, the L-P titration method and DMAC method resulted in higher quantitative values than the F-C method and BSA precipitation method, respectively. However, ratios of results obtained by the L-P and F-C method ranged from 1 to 28, and ratios of results obtained by the DMAC and BSA precipitation method ranged from <1 to 280. This tremendous variation is likely due to variation in polyphenol composition and sample matrix. This information provides perspective for comparison of results obtained through these different methods, and a basis for choosing the most appropriate analytical method for quantification of polyphenols to address a specific research question when working with commercial fruit juice, apple juice from different apple cultivars, and commercial ciders. PRACTICAL APPLICATION: This study compared results obtained when four common polyphenol quantification methods were applied to a diverse selection of fruit juices and beverages with distinct polyphenol composition and sample matrix. The matrix and polyphenol composition of the samples significantly influenced the results. Our findings can help manufacturers of fruit-based products choose the most appropriate analytical method for polyphenol quantification as part of a quality assurance program or to convey information on dietary polyphenol content to consumers. An assessment of analytical method validation parameters is provided for each of the four methods, which will help users of these methods to understand their limitations.

From Academia to Reality Check: A Theoretical Framework on the Use of Chemometric in Food Sciences

There is no doubt that the current knowledge in chemistry, biochemistry, biology, and mathematics have led to advances in our understanding about food and food systems. However, the so-called reductionist approach has dominated food research, hindering new developments and innovation in the field. In the last three decades, food science has moved into the digital and technological era, inducing several challenges resulting from the use of modern instrumental techniques, computing and algorithms incorporated to the exploration, mining, and description of data derived from this complexity. In this environment, food scientists need to be mindful of the issues (advantages and disadvantages) involved in the routine applications of chemometrics. The objective of this opinion paper is to give an overview of the key issues associated with the implementation of chemometrics in food research and development. Please note that specifics about the different methodologies and techniques are beyond the scope of this review.

An Artificial Neural Network Approach to Predict the Effects of Formulation and Process Variables on Prednisone Release from a Multipartite System

The impact of formulation and process variables on the in-vitro release of prednisone from a multiple-unit pellet system was investigated. Box-Behnken Response Surface Methodology (RSM) was used to generate multivariate experiments. The extrusion-spheronization method was used to produce pellets and dissolution studies were performed using United States Pharmacopoeia (USP) Apparatus 2 as described in USP XXIV. Analysis of dissolution test samples was performed using a reversed-phase high-performance liquid chromatography (RP-HPLC) method. Four formulation and process variables viz., microcrystalline cellulose concentration, sodium starch glycolate concentration, spheronization time and extrusion speed were investigated and drug release, aspect ratio and yield were monitored for the trained artificial neural networks (ANN). To achieve accurate prediction, data generated from experimentation were used to train a multi-layer perceptron (MLP) using back propagation (BP) and the Broyden-Fletcher-Goldfarb-Shanno (BFGS) 57 training algorithm until a satisfactory value of root mean square error (RMSE) was observed. The study revealed that the in-vitro release profile of prednisone was significantly impacted by microcrystalline cellulose concentration and sodium starch glycolate concentration. Increasing microcrystalline cellulose concentration retarded dissolution rate whereas increasing sodium starch glycolate concentration improved dissolution rate. Spheronization time and extrusion speed had minimal impact on prednisone release but had a significant impact on extrudate and pellet quality. This work demonstrated that RSM can be successfully used concurrently with ANN for dosage form manufacture to permit the exploration of experimental regions that are omitted when using RSM alone.

The fate of Bacillus cereus and Geobacillus stearothermophilus during alkalization of cocoa as affected by alkali concentration and use of pre-roasted nibs

Alkalization is a step of cocoa processing and consists of the use of alkali and high temperature to improve the sensorial and technological qualities of cocoa. Intense food processing can select spores, which can compromise safety and quality of the final product. Thus, the aim of this study was to evaluate the fate of B. cereus and G. stearothermophilus spores during the alkalization of pre-roasted (Pr) nibs (held at 120 °C) and unroasted (Ur) nibs (held at 90 °C) using potassium carbonate (0, 2, 4 and 6% w/w). In all conditions, log-linear inactivation kinetics with a tail was observed. The inactivation rate (k_max) for B. cereus varied from 0.065 to 1.67 min^-1, whereas the k_max for G. stearothermophilus varied from 0.012 to 0.063 min^-1. For both microorganisms, the lowest k_max values were observed during Ur nibs alkalization. The carbonate concentration increase promoted k_max values reduction. The highest tail values were observed for G. stearothermophilus in Ur nibs alkalization, reaching 3.04 log spores/g. Tail formation and low k_max values indicated that cocoa alkalization does not cause significant reductions on bacterial spore population. Therefore, the microbiological control should be primarily ensured by the raw material quality and by avoiding recontamination in the cocoa chain.

Optimization of ultrasound-assisted extraction of phenolic compounds from grapefruit (Citrus paradisi Macf.) leaves via D-optimal design and artificial neural network design with categorical and quantitative variables

BACKGROUND

The extraction of phenolic compounds from grapefruit leaves assisted by ultrasound-assisted extraction (UAE) was optimized using response surface methodology (RSM) by means of D-optimal experimental design and artificial neural network (ANN). For this purpose, five numerical factors were selected: ethanol concentration (0-50%), extraction time (15-60 min), extraction temperature (25-50 °C), solid:liquid ratio (50-100 g L^-1 ) and calorimetric energy density of ultrasound (0.25-0.50 kW L^-1 ), whereas ultrasound probe horn diameter (13 or 19 mm) was chosen as categorical factor.

RESULTS

The optimized experimental conditions yielded by RSM were: 10.80% for ethanol concentration; 58.52 min for extraction time; 30.37 °C for extraction temperature; 52.33 g L^-1 for solid:liquid ratio; 0.457 kW L^-1 for ultrasonic power density, with thick probe type. Under these conditions total phenolics content was found to be 19.04 mg gallic acid equivalents g^-1 dried leaf.

CONCLUSION

The same dataset was used to train multilayer feed-forward networks using different approaches via MATLAB, with ANN exhibiting superior performance to RSM (differences included categorical factor in one model and higher regression coefficients), while close values were obtained for the extraction variables under study, except for ethanol concentration and extraction time. © 2018 Society of Chemical Industry.

In vitro antioxidant and antihypertensive compounds from camu-camu (Myrciaria dubia McVaugh, Myrtaceae) seed coat: A multivariate structure-activity study

Camu-camu (Myrciaria dubia) pulp, seeds, and skin are widely known because of their nutritional properties. However, the seed coat has never been studied as a source of bioactive compounds. Herein, we characterized the phenolic composition, the antioxidant activity, and inhibition of angiotensin-converting enzyme (ACE) of three different extracts (water, propanone, and ethanol) from this residue and assessed the structure-activity using bivariate and multivariate statistical approaches. Phenolic acids and flavonoids were quantified by high-performance liquid chromatography while the ferric reducing antioxidant power (FRAP), inhibition of lipid peroxidation using egg yolk and Wistar rat brain, scavenging of 2,2-diphenyl-1-picrylhydrazyl (DPPH^•) radical, Folin-Ciocalteu reducing capacity (FCRC), and the inhibition of angiotensin-converting enzyme (ACE) by the extracts were also analyzed. t-Resveratrol was found in camu-camu seed coat for the first time. The aqueous extract had the highest total phenolic content, FRAP, DPPH^•, FCRC, and inhibition of lipid oxidation using both chemical and biological assays, while the propanone extract showed the opposite behavior but it presented higher in vitro antihypertensive activity. The ethanolic extract exhibited intermediate values for the responses. The association between chemical composition and the functional properties of the camu-camu seed coat extracts were revealed using correlation analysis and principal component analysis.

Bioinactivation FE: A free web application for modelling isothermal and dynamic microbial inactivation

Mathematical models developed in predictive microbiology are nowadays an essential tool for food scientists and researchers. However, advanced knowledge of scientific programming and mathematical modelling are often required in order to use them, especially in cases of modelling of dynamic and/or non-linear processes. This may be an obstacle for food scientists without such skills. Scientific software can help making these tools more accessible for scientists lacking a deep mathematical or computing background. Recently, the R package bioinactivation was published, including functions (model fitting and predictions) for modelling microbial inactivation under isothermal or dynamic conditions. It was uploaded to the Comprehensive R Archive Network (CRAN), but users need basic R programming knowledge in order to use it. Therefore, it was accompanied by Bioinactivation SE, a user-friendly web application including selected functions in the software for users without a programming background. In this work, a new web application, Bioinactivation FE, is presented. It is an extension of Bioinactivation SE which includes an interface to every function in the bioinactivation package: model fitting of isothermal and non-isothermal experiments, and generation of survivor curves and prediction intervals. Moreover, it includes several improvements in the user interface based on the users' feedback. The capabilities of the software are demonstrated through two case studies using data published in the scientific literature. In the first case study, the response of Escherichia coli to isothermal and non-isothermal treatments is compared, illustrating the presence of an induced thermal resistance. In the second, the effect of nanoemulsified d-limonene on the thermal resistance of Salmonella Senftenberg is quantified.

Assessment of Salmonella spp. and Escherichia coli O157:H7 growth on lettuce exposed to isothermal and non-isothermal conditions

This study aimed to assess the growth of Salmonella and Escherichia coli O157:H7 on lettuce exposed to isothermal and non-isothermal conditions. Pathogens were inoculated on lettuce separately and stored under isothermal condition at 5 °C, 10 °C, 25 °C, 37 °C for both bacteria, at 40 °C for Salmonella and 42 °C for E. coli O157:H7. Growth curves were built by fitting the data to the Baranyi's DMFit, generating R2 values greater than 0.92 for primary models. Secondary models were fitted with Ratkowsky equations, generating R2 values higher than 0.91 and RMSE lower than 0.1. Experimental data showed that both bacteria could grow at all temperatures. Also, the growth of both pathogens under non-isothermal conditions was studied simulating temperatures found from harvest to supermarkets in Brazil. Models were analysed by R2, RMSE, bias factor (Bf) and accuracy factor (Af). Salmonella and E. coli O157:H7 were able to grow in this temperature profile and the models could predict the behavior of these microorganisms on lettuce under isothermal and non-isothermal conditions. Based on the results, a negligible growth time (ς) was proposed to provide the time which lettuce could be exposed to a specific temperature and do not present an expressive growth of bacteria. The ς was developed based on Baranyi's primary model equation and on growth potential concept. ς is the value of lag phase added of the time necessary to population grow 0.5 log CFU/g. The ς of lettuce exposed to 37 °C was 1.3 h, while at 5 °C was 3.3 days.