Effect of wheat roasting conditions and wheat type on short-wave infrared (SWIR) spectral data of whole and milled wheat by ANOVA-simultaneous component analysis.

ANOVA-simultaneous component analysis (ASCA) was used to investigate the effect of roasting and wheat type on shortwave-infrared (SWIR) spectra of whole wheat and flour through assessment of statistical significance and characterisation of the contributing spectral features. The full factorial experimental design included two wheat types, three roasting temperatures and three roasting frequencies. SWIR spectral images were collected from the two roasted wheat types and their two milled samples. Three ASCA models, one for each wheat conformation (kernel, whole wheat flour, white flour) were investigated. It was evidenced that all factors and interaction in the whole wheat flour model had a significant (p ≤ 0.05) effect on spectral data. Only the factor roasting frequency was not significant in white flour model and only the interaction between roasting frequency and wheat type was not significant for the kernel model. The main variations in the loading line plots were identified and characterised by chemical structural differences that occur within the sample. The effect of roasting frequency had a more adverse effect on protein stability, moisture evaporation, water soluble carbohydrates and aromatic amino acids, compared to roasting temperature. A Rapid Visco-Analyser (RVA) was used to further investigate difference in wheat type as almost all spectral data sets differed significantly. The most prominent difference between the two wheat types was observed as differences in amylase activity and presence of lipids. ASCA applied to SWIR whole wheat and flour spectral data effectively characterised the significant effect of roasting on wheat starch and protein structures.

Application of segmented analysis via multivariate curve resolution with alternating least squares to 1H-nuclear magnetic resonance spectroscopy to identify different sugar sources.

The different types of sugar employed in the food industry exhibit chemical similarity and are mostly dominated by sucrose. Owing to the sugar origin of and differences in production, the presence of certain minor organic compounds differs. To differentiate between sugars based on their botanical source, geographical origin, or storage conditions, commercial brown sugars and sugar beet extracts were analyzed by 1H NMR spectroscopy applying a segmented analysis by means of multivariate curve resolution-alternating least squares (MCR-ALS). Principal component analysis and partial least squares-discriminant analysis yielded excellent differentiation between sugars from different sources after the application of this preprocessing strategy; without loss of chemical information and with direct interpretation of the results. By applying a segmented analysis via MCR-ALS to 1H NMR sugar data, similar spectroscopic profiles could be differentiated. This improved the selectivity of 1H NMR spectroscopy for sugar source differentiation which can be useful for industrial sugar authentication purposes.

Classification and quantification of sucrose from sugar beet and sugarcane using optical spectroscopy and chemometrics.

Sucrose, obtained from either sugar beet or sugarcane, is one of the main ingredients used in the food industry. Due to the same molecular structure, chemical methods cannot distinguish sucrose from both sources. More practical and affordable methods would be valuable. Sucrose samples (cane and beet) were collected from nine countries, 25% (w/w) aqueous solutions were prepared and their absorbances recorded from 200 to 1380 nm. Spectral differences were observable in the ultraviolet-visible (UV-Vis) region from 200 to 600 nm due to impurities in sugar. Linear discriminant analysis (LDA), classification and regression trees, and soft independent modeling of class analogy were tested for the UV-Vis region. All methods showed high performance accuracies. LDA, after selection of five wavelengths, gave 100% correct classification with a simple interpretation. In addition, binary mixtures of the sugar samples were prepared for quantitative analysis by means of partial least squares regression and multiple linear regression (MLR). MLR with first derivative Savitzky-Golay were most acceptable with root mean square error of cross-validation, prediction, and the ratio of (standard error of) prediction to (standard) deviation values of 3.92%, 3.28%, and 9.46, respectively. Using UV-Vis spectra and chemometrics, the results show promise to distinguish between the two different sources of sucrose. An affordable and quick analysis method to differentiate between sugars, produced from either sugar beet or sugarcane, is suggested. This method does not involve complex chemical analysis or high-level experts and can be used in research or by industry to detect the source of the sugar which is important for some countries' agricultural policies.

Wheat starch structure-function relationship in breadmaking: A review.

Bread dough and bread are dispersed systems consisting of starch polymers that interact with other flour components and added ingredients during processing. In addition to gluten proteins, starch impacts the quality characteristics of the final baked product. Wheat starch consists of amylose and amylopectin organized into alternating semicrystalline and amorphous layers in granules that vary in size and are embedded in the endosperm protein matrix. Investigation of the molecular movement of protons in the dough system provides a comprehensive insight into granular swelling and amylose leaching. Starch interacts with water, proteins, amylase, lipids, yeast, and salt during various stages of breadmaking. As a result, the starch polymers within the produced crumb and crust, together with the rate of retrogradation and staling due to structural reorganization, moisture migration, storage temperature, and relative humidity determines the final product's textural perception. This review aims to provide insight into wheat starch composition and functionality and critically review recently published research results with reference to starch structure-function relationship and factors affecting it during dough formation, fermentation, baking, cooling, and storage of bread.

Raw Beef Patty Analysis Using Near-Infrared Hyperspectral Imaging: Identification of Four Patty Categories.

South African legislation regulates the classification/labelling and compositional specifications of raw beef patties, to combat processed meat fraud and to protect the consumer. A near-infrared hyperspectral imaging (NIR-HSI) system was investigated as an alternative authentication technique to the current destructive, time-consuming, labour-intensive and expensive methods. Eight hundred beef patties (ca. 100 g) were made and analysed to assess the potential of NIR-HSI to distinguish between the four patty categories (200 patties per category): premium 'ground patty'; regular 'burger patty'; 'value-burger/patty' and the 'econo-burger'/'budget'. Hyperspectral images were acquired with a HySpex SWIR-384 (short-wave infrared) imaging system using the Breeze® acquisition software, in the wavelength range of 952-2517 nm, after which the data was analysed using image analysis, multivariate techniques and machine learning algorithms. It was possible to distinguish between the four patty categories with accuracies ≥97%, indicating that NIR-HSI offers an accurate and reliable solution for the rapid identification and authentication of processed beef patties. Furthermore, this study has the potential of providing an alternative to the current authentication methods, thus contributing to the authenticity and fair-trade of processed meat products locally and internationally.

Phenolic profiling and antioxidant evaluation of extracts from Southern African indigenous fruits byproducts.

The call to build climate-resilient food systems in Africa has revived interest in indigenous fruits, which, however, remain under-researched. In this study, the phenolic content and antioxidant profiles of seed and pulp of ethanolic extracts from eight Southern African indigenous fruits were evaluated using UPLC-ESI-Q-TOF/MS and four antioxidant assays. Total phenols and hydroxycinnamic acids were highest in Dovyalis caffra (Kei apple) seed (5084.5 and 3403.83 mg/kg). Flavonoids were most abundant in Colpoon compressum (Colpoon) seed (1127.23 mg/kg), while hydrolysable tannins were highest in Syzygium guineense (Water pear) seed (666.13 mg/kg). Proanthocyanidins were abundant in Harpephyllum caffrum (Wild plum) pulp while anthocyanins were highest in Olea africana (Wild olive) pulp. Hierarchical clustering heatmap analysis showed similar concentration and diversity in the composition of reported compounds. Syzygium guineense seed had the lowest DPPH values. ORAC values were highest in O. africana pulp while H. caffrum pulp had the highest FRAP values and lipoxygenase inhibition capacity. In conclusion, the study revealed a diverse profile of phenolics in indigenous fruits extracts, to which their bioactivity is attributed. Specifically, H. caffrum pulp and S. guineense seed have potential as natural sources of phenolic antioxidants for food application.

Holistic View of Starch Chemistry, Structure and Functionality in Dry Heat-Treated Whole Wheat Kernels and Flour.

Heat treatment is used as a pre-processing step to beneficially change the starch properties of wheat flour to enhance its utilisation in the food industry. Heat-treated wheat flour may provide improved eating qualities in final wheat-based products since flour properties predominantly determine the texture and mouthfeel. Dry heat treatment of wheat kernels or milled wheat products involves heat transfer through means of air, a fluidising medium, or radiation-often resulting in moisture loss. Heat treatment leads to changes in the chemical, structural and functional properties of starch in wheat flour by inducing starch damage, altering its molecular order (which influences its crystallinity), pasting properties as well as its retrogradation and staling behaviour. Heat treatment also induces changes in gluten proteins, which may alter the rheological properties of wheat flour. Understanding the relationship between heat transfer, the thermal properties of wheat and the functionality of the resultant flour is of critical importance to obtain the desired extent of alteration of wheat starch properties and enhanced utilisation of the flour. This review paper introduces dry heat treatment methods followed by a critical review of the latest published research on heat-induced changes observed in wheat flour starch chemistry, structure and functionality.

Biopreservative efficacy of grape (Vitis vinifera) and clementine mandarin orange (Citrus reticulata) by-product extracts in raw ground beef patties.

Beef patties were treated with 450 µg/g of extracts from grape (Vitis vinifera) seeds (GSE), pomace (GPE) or orange (Citrus reticulata) pomace (OPE) and compared to negative (no extract; CTR) and positive (sodium metabisulphite; SMB) controls for their effect on colour, lipid and protein oxidation and bacterial growth under simulated retail display conditions (4 °C) for 9 d, and sensory quality. Antioxidant activity and redness of beef patties increased in the order of CTR < OPE = GPE < GSE < SMB. The order of thiobarbituric acid reactive substances and carbonyl values were CTR > GPE = OPE > GSE > SBM, while that of bacterial counts were CTR > GSE = GPE > OPE > SMB. Retail display period had significant effect on all the shelf-life parameters. Overall, intensity of aroma, beef-like aroma and flavour in beef patties were highest in OPE. Results suggested that GSE and OPE could be commercially valorised as natural antioxidants and antibacterials in beef patties, respectively.

Non-Destructive Spectroscopic and Imaging Techniques for the Detection of Processed Meat Fraud.

In recent years, meat authenticity awareness has increased and, in the fight to combat meat fraud, various analytical methods have been proposed and subsequently evaluated. Although these methods have shown the potential to detect low levels of adulteration with high reliability, they are destructive, time-consuming, labour-intensive, and expensive. Therefore, rendering them inappropriate for rapid analysis and early detection, particularly under the fast-paced production and processing environment of the meat industry. However, modern analytical methods could improve this process as the food industry moves towards methods that are non-destructive, non-invasive, simple, and on-line. This review investigates the feasibility of different non-destructive techniques used for processed meat authentication which could provide the meat industry with reliable and accurate real-time monitoring, in the near future.

Application of ANOVA-simultaneous component analysis to quantify and characterise effects of age, temperature, syrup adulteration and irradiation on near-infrared (NIR) spectral data of honey.

NIR spectroscopy combined with chemometric analysis has proven to be a rapid and cost-effective screening tool for the detection of syrup-adulterated honey. Processing and storage conditions which alter the chemical and physical state of honey may affect the spectra. The effects of age, storage temperature, syrup adulteration (10 and 20% w/w) and irradiation treatment on the NIR spectra of honey were investigated as a function of time with ANOVA-simultaneous component analysis (ASCA), an experimental design-focused exploratory data analysis method. The factors 'time', 'temperature' and 'adulteration' were found to have significant effects (p < 0.05), but no significant effect was observed for irradiation treatment. A significant interaction effect was found between factors 'time' and 'adulteration', with the greatest disparity between authentic and adulterated class signals found immediately after adulteration and decreasing within three months thereafter.

Differentiation of South African Game Meat Using Near-Infrared (NIR) Spectroscopy and Hierarchical Modelling.

Near-infrared (NIR) spectroscopy, combined with multivariate data analysis techniques, was used to rapidly differentiate between South African game species, irrespective of the treatment (fresh or previously frozen) or the muscle type. These individual classes (fresh; previously frozen; muscle type) were also determined per species, using hierarchical modelling. Spectra were collected with a portable handheld spectrophotometer in the 908-1676-nm range. With partial least squares discriminant analysis models, we could differentiate between the species with accuracies ranging from 89.8%-93.2%. It was also possible to distinguish between fresh and previously frozen meat (90%-100% accuracy). In addition, it was possible to distinguish between ostrich muscles (100%), as well as the forequarters and hindquarters of the zebra (90.3%) and springbok (97.9%) muscles. The results confirm NIR spectroscopy's potential as a rapid and non-destructive method for species identification, fresh and previously frozen meat differentiation, and muscle type determination.

Classification of white maize defects with multispectral imaging.

Multispectral imaging with object-wise multivariate image analysis was evaluated for its potential to grade whole white maize kernels. The types of defective materials regarded in grading legislation were divided into 13 classes, and were imaged with a multispectral imaging instrument spanning the UV, visible and NIR regions (19 wavelengths ranging from 375 to 970nm). Object-wise partial least squares discriminant analysis (PLS-DA) models were developed and validated with an independent data set. Results demonstrated good performance in distinguishing between sound maize and undesirable materials, with cross-validated coefficients of determination (Q2) and classification accuracies ranging from 0.35 to 0.99 and 83 to 100%, respectively. Wavelengths related to absorbance of green, yellow and orange colour indicated the presence of lycopene and anthocyanin (505, 525, 570 and 590 nm). NIR wavelengths 890, 940 nm (associated with fat) and 970 nm (associated with water) were generally identified as important features throughout the study.

Near infrared hyperspectral imaging in quality and safety evaluation of cereals.

The requirements of cereal research, as well as grading and evaluation of food products, have encouraged the development of nondestructive, rapid, and accurate analytical techniques to evaluate grain quality and safety. NIR hyperspectral imaging integrates spectroscopy and imaging techniques in one analytical system, allowing direct identification of chemical components and their distribution within the sample. It is a promising technique that may be implemented on-line, enabling the cereal industry to move away from subjective, manual classification and measuring methods. NIR hyperspectral imaging has gained popularity for rapidly acquiring information to enable the quantification, identification or differentiation of a variety of cereal properties. The technique can potentially replace multiple conventional chemical, microbial or physical tests with a single, automated image acquisition. Individual kernels can be analyzed nondestructively, enabling one to follow changes in the same kernel over time (e.g. fungal development). Although NIR hyperspectral imaging has not been extensively implemented in industry, it shows great potential for the development of an evaluation system to assess cereal grains, especially regarding variety discrimination and grading/classification properties. This review outlines the theory and principles of NIR hyperspectral imaging, and focuses specifically on its application in cereal science research and industry.

Non-destructive techniques for the detection of fungal infection in cereal grains.

Infection of cereal grains by fungi is a serious problem worldwide. Depending on the environmental conditions, cereal grains may be colonised by different species of fungi. These fungi cause reduction in yield, quality and nutritional value of the grain; and of major concern is their production of mycotoxins which are harmful to both humans and animals. Early detection of fungal contamination is an essential control measure for ensuring storage longevity and food safety. Conventional methods for detection of fungal infection, such as culture and colony techniques or immunological methods are either slow, labour intensive or difficult to automate. In recent years, there has been an increasing need to develop simple, rapid, non-destructive methods for early detection of fungal infection and mycotoxins contamination in cereal grains. Methods such as near infrared (NIR) spectroscopy, NIR hyperspectral imaging, and electronic nose were evaluated for these purposes. This paper reviews the different non-destructive techniques that have been considered thus far for detection of fungal infection and mycotoxins in cereal grains, including their principles, application and limitations.

Differentiation of foodborne bacteria using NIR hyperspectral imaging and multivariate data analysis.

The potential for near-infrared (NIR) hyperspectral imaging and multivariate data analysis to be used as a rapid non-destructive tool for detection and differentiation of bacteria was investigated. NIR hyperspectral images were collected of Bacillus cereus, Escherichia coli, Salmonella enteritidis, Staphylococcus aureus and Staphylococcus epidermidis grown on agar for 20 h at 37 °C. Principal component analysis (PCA) was applied to mean-centred data. Standard normal variate (SNV) correction and the Savitzky-Golay technique was applied (2nd derivative, 3rd-order polynomial; 25 point smoothing) to wavelengths in the range of 1103 to 2471 nm. Chemical differences between colonies which appeared similar in colour on growth media (B. cereus, E. coli and S. enteritidis.) were evident in the PCA score plots. It was possible to distinguish B. cereus from E. coli and S. enteritidis along PC1 (59 % sum of squares (SS)) and between E. coli and S. enteritidis in the direction of PC2 (6.85 % SS). S. epidermidis was separated from B. cereus and S. aureus along PC1 (37.5 % SS) and was attributed to variation in amino acid and carbohydrate content. Two clusters were evident in the PC1 vs. PC2 PCA score plot for the images of S. aureus and S. epidermidis, thus permitting distinction between species. Differentiation between genera (similarly coloured on growth media), Gram-positive and Gram-negative bacteria and pathogenic and non-pathogenic species was possible using NIR hyperspectral imaging. Partial least squares discriminant analysis (PLS-DA) models were used to confirm the PCA data. The best predictions were made for B. cereus and Staphylococcus species, where results ranged from 82.0 to 99.96 % correctly predicted pixels.

Assessing similarity analysis of chromatographic fingerprints of Cyclopia subternata extracts as potential screening tool for in vitro glucose utilisation.

Similarity analysis of the phenolic fingerprints of a large number of aqueous extracts of Cyclopia subternata, obtained by high-performance liquid chromatography (HPLC), was evaluated as a potential tool to screen extracts for relative bioactivity. The assessment was based on the (dis)similarity of their fingerprints to that of a reference active extract of C. subternata, proven to enhance glucose uptake in vitro and in vivo. In vitro testing of extracts, selected as being most similar (n = 5; r ≥ 0.962) and most dissimilar (n = 5; r ≤ 0.688) to the reference active extract, showed that no clear pattern in terms of relative glucose uptake efficacy in C2C12 myocytes emerged, irrespective of the dose. Some of the most dissimilar extracts had higher glucose-lowering activity than the reference active extract. Principal component analysis revealed the major compounds responsible for the most variation within the chromatographic fingerprints, as mangiferin, isomangiferin, iriflophenone-3-C-ß-D-glucoside-4-O-ß-D-glucoside, iriflophenone-3-C-ß-D-glucoside, scolymoside, and phloretin-3',5'-di-C-ß-D-glucoside. Quantitative analysis of the selected extracts showed that the most dissimilar extracts contained the highest mangiferin and isomangiferin levels, whilst the most similar extracts had the highest scolymoside content. These compounds demonstrated similar glucose uptake efficacy in C2C12 myocytes. It can be concluded that (dis)similarity of chromatographic fingerprints of extracts of unknown activity to that of a proven bioactive extract does not necessarily translate to lower or higher bioactivity.

Application of Rapid Visco Analyser (RVA) viscograms and chemometrics for maize hardness characterisation.

It has been established in this study that the Rapid Visco Analyser (RVA) can describe maize hardness, irrespective of the RVA profile, when used in association with appropriate multivariate data analysis techniques. Therefore, the RVA can complement or replace current and/or conventional methods as a hardness descriptor. Hardness modelling based on RVA viscograms was carried out using seven conventional hardness methods (hectoliter mass (HLM), hundred kernel mass (HKM), particle size index (PSI), percentage vitreous endosperm (%VE), protein content, percentage chop (%chop) and near infrared (NIR) spectroscopy) as references and three different RVA profiles (hard, soft and standard) as predictors. An approach using locally weighted partial least squares (LW-PLS) was followed to build the regression models. The resulted prediction errors (root mean square error of cross-validation (RMSECV) and root mean square error of prediction (RMSEP)) for the quantification of hardness values were always lower or in the same order of the laboratory error of the reference method.

Chemometric analysis of chromatographic fingerprints shows potential of Cyclopia maculata (Andrews) Kies for production of standardized extracts with high xanthone content.

Cyclopia species are used for the production of honeybush tea and food ingredient extracts associated with many health benefits. A species-specific high-performance liquid chromatography (HPLC) method for Cyclopia maculata, developed and validated, allowed quantification of the major compounds in extracts from "unfermented" and fermented C. maculata. Two xanthones were tentatively identified for the first time in a Cyclopia species, whereas an additional four compounds were tentatively identified for the first time in C. maculata. "Fermentation" (oxidation) decreased the content of all compounds, with the exception of vicenin-2. Similarity analysis of the chromatographic fingerprints of unfermented C. maculata aqueous extracts showed extremely low variation (r ≥ 0.97) between samples. Some differences between wild-harvested and cultivated seedling plants were, however, demonstrated using principal component analysis. Quantitative data of selected compounds confirmed the low level of variation, making this Cyclopia species ideal for the production of standardized food ingredient extracts.

Near-infrared spectroscopy and hyperspectral imaging: non-destructive analysis of biological materials.

Near-infrared (NIR) spectroscopy has come of age and is now prominent among major analytical technologies after the NIR region was discovered in 1800, revived and developed in the early 1950s and put into practice in the 1970s. Since its first use in the cereal industry, it has become the quality control method of choice for many more applications due to the advancement in instrumentation, computing power and multivariate data analysis. NIR spectroscopy is also increasingly used during basic research performed to better understand complex biological systems, e.g. by means of studying characteristic water absorption bands. The shorter NIR wavelengths (800-2500 nm), compared to those in the mid-infrared (MIR) range (2500-15 000 nm) enable increased penetration depth and subsequent non-destructive, non-invasive, chemical-free, rapid analysis possibilities for a wide range of biological materials. A disadvantage of NIR spectroscopy is its reliance on reference methods and model development using chemometrics. NIR measurements and predictions are, however, considered more reproducible than the usually more accurate and precise reference methods. The advantages of NIR spectroscopy contribute to it now often being favoured over other spectroscopic (colourimetry and MIR) and analytical methods, using chemicals and producing chemical waste, such as gas chromatography (GC) and high performance liquid chromatography (HPLC). This tutorial review intends to provide a brief overview of the basic theoretical principles and most investigated applications of NIR spectroscopy. In addition, it considers the recent development, principles and applications of NIR hyperspectral imaging. NIR hyperspectral imaging provides NIR spectral data as a set of images, each representing a narrow wavelength range or spectral band. The advantage compared to NIR spectroscopy is that, due to the additional spatial dimension provided by this technology, the images can be analysed and visualised as chemical images providing identification as well as localisation of chemical compounds in non-homogenous samples.

Applications of single kernel conventional and hyperspectral imaging near infrared spectroscopy in cereals.

Single kernel (SK) near infrared (NIR) reflectance and transmittance technologies have been developed during the last two decades for a range of cereal grain physical quality and chemical traits as well as detecting and predicting levels of toxins produced by fungi. Challenges during the development of single kernel near infrared (SK-NIR) spectroscopy applications are modifications of existing NIR technology to present single kernels for scanning as well as modifying reference methods for the trait of interest. Numerous applications have been developed, and cover almost all cereals although most have been for key traits including moisture, protein, starch and oil in the globally important food grains, i.e. maize, wheat, rice and barley. An additional benefit in developing SK-NIR applications has been to demonstrate the value in sorting grain infected with a fungus or mycotoxins such as deoxynivalenol, fumonisins and aflatoxins. However, there is still a need to develop cost-effective technologies for high-speed sorting which can be used for small grain samples such as those from breeding programmes or commercial sorting; capable of sorting tonnes per hour. Development of SK-NIR technologies also includes standardisation of SK reference methods to analyse single kernels. For protein content, the use of the Dumas method would require minimal standardisation; for starch or oil content, considerable development would be required. SK-NIR, including the use of hyperspectral imaging, will improve our understanding of grain quality and the inherent variation in the range of a trait. In the area of food safety, this technology will benefit farmers, industry and consumers if it enables contaminated grain to be removed from the human food chain.