Sensomics - From conventional to functional NIR spectroscopy - Shining light over the aroma and taste of foods

Analyzing the Quality Parameters of Apples by Spectroscopy from Vis/NIR to NIR Region: A Comprehensive Review

Spectroscopic methods deliver a valuable non-destructive analytical tool that provides simultaneous qualitative and quantitative characterization of various samples. Apples belong to the world's most consumed crops and with the current challenges of climate change and human impacts on the environment, maintaining high-quality apple production has become critical. This review comprehensively analyzes the application of spectroscopy in near-infrared (NIR) and visible (Vis) regions, which not only show particular potential in evaluating the quality parameters of apples but also in optimizing their production and supply routines. This includes the assessment of the external and internal characteristics such as color, size, shape, surface defects, soluble solids content (SSC), total titratable acidity (TA), firmness, starch pattern index (SPI), total dry matter concentration (DM), and nutritional value. The review also summarizes various techniques and approaches used in Vis/NIR studies of apples, such as authenticity, origin, identification, adulteration, and quality control. Optical sensors and associated methods offer a wide suite of solutions readily addressing the main needs of the industry in practical routines as well, e.g., efficient sorting and grading of apples based on sweetness and other quality parameters, facilitating quality control throughout the production and supply chain. This review also evaluates ongoing development trends in the application of handheld and portable instruments operating in the Vis/NIR and NIR spectral regions for apple quality control. The use of these technologies can enhance apple crop quality, maintain competitiveness, and meet the demands of consumers, making them a crucial topic in the apple industry. The focal point of this review is placed on the literature published in the last five years, with the exceptions of seminal works that have played a critical role in shaping the field or representative studies that highlight the progress made in specific areas.

Sweet Chestnut (Castanea sativa Mill.) Nutritional and Phenolic Composition Interactions with Chestnut Flavor Physiology

The European chestnut (Castanea sativa Mill.), is an environmentally and economically important species in Europe, mainly for fruit production. The chestnut fruit is well-known for its nutritional properties, namely its high concentration of carbohydrates (starch) and its low-fat content, as well as being one of the few fruits that do not contain gluten. Due to its chemical and nutritional characteristics beneficial to health, the sweet chestnut is a food recommended at different levels. The biochemistry of the mouth and nose of a human being is very complex. However, understanding the different interactions between the biochemistry of our sensory organs and food helps us to comprehend certain concepts, such as flavor and how it is involved in the sensory evaluation of the chestnuts. For the selection of high-quality products, it is necessary to develop reliable methods both from a qualitative and sensory point of view, and chestnut is a fruit with unique sensory characteristics that can be used in various gastronomic dishes, from main courses to desserts.

Recent advances in analytical strategies for coffee volatile studies: Opportunities and challenges

Coffee has attracted significant research interest owing to its complex volatile composition and aroma, which imparts a pleasant sensorial experience that remains challenging to analyse and interpret. This review summarises analytical challenges associated with coffee's volatile and matrix complexity, and recent developments in instrumental techniques to resolve them. The benefits of state-of-the-art analytical techniques applied to coffee volatile analysis from experimental design to sample preparation, separation, detection, and data analysis are evaluated. Complementary method selection coupled with progressive experimental design and data analysis are vital to unravel the increasing comprehensiveness of coffee volatile datasets. Considering this, analytical workflows for conventional, targeted, and untargeted coffee volatile analyses are thus proposed considering the trends towards sorptive extraction, multidimensional gas chromatography, and high-resolution mass spectrometry. In conclusion, no single analytical method addresses coffee's complexity in its entirely, and volatile analysis must be tailored to the key objectives and concerns of the analyst.

Application of NIR Spectral Standardization Based on Principal Component Score Evaluation in Wheat Flour Crude Protein Model Sharing

In order to explore spectral standardization methods for spectra collected by different NIR spectrometers, to reduce spectral differences, and to realize model sharing among different instruments, the crude protein content of 154 wheat flour samples was measured using one grating and three Fabry-Perot tunable filter NIR spectrometers in wavelength. At the same wavelength range and wavelength interval, three algorithms, namely, direct standardization (DS), piecewise direct standardization (PDS), and simple linear regression direct standardization (SLRDS), were used to standardize spectra collected by different instruments from the same samples. Spectral standardization error rate (SSER), principal component score error rate (PCSER), and other indicators were employed to analyze the spectral differences between the master and the target spectra, and the effect of model sharing was evaluated using parameters including prediction correlation coefficient (Rp), root mean square error of prediction (RMSEP), and relative prediction deviation (RPD). The results show the following: (1) The difference between spectra can be quantitatively evaluated through analyzing SSER and PCSER. (2) After standardization by the three algorithms, the spectral difference between the three target and the master spectrometers is significantly reduced and the prediction effect of the master model is greatly improved. (3) Among the three algorithms, DS algorithm had the smallest error rate in standardizing spectra from three target spectrometers. After standardization by the DS algorithm, the master model had the best effect. Its prediction accuracy was greatly improved compared with that before standardization. (4) The standard model established based on the S450 spectrometer can be applied to the same spectrometer as the N500 spectrometer with the same resolution and different wavelength ranges, so as to achieve model sharing. Therefore, DS, PDS, and SLRDS algorithms can effectively reduce the spectral differences between different instruments and realize the sharing of NIR calibration models for wheat flour crude protein measurement.

Chestnut Cultivar Identification through the Data Fusion of Sensory Quality and FT-NIR Spectral Data

The world production of chestnuts has significantly grown in recent decades. Consumer attitudes, increasingly turned towards healthy foods, show a greater interest in chestnuts due to their health benefits. Consequently, it is important to develop reliable methods for the selection of high-quality products, both from a qualitative and sensory point of view. In this study, Castanea spp. fruits from Italy, namely Sweet chestnut cultivar and the Marrone cultivar, were evaluated by an official panel, and the responses for sensory attributes were used to verify the correlation to the near-infrared spectra. Data fusion strategies have been applied to take advantage of the synergistic effect of the information obtained from NIR and sensory analysis. Large nuts, easy pellicle removal, chestnut aroma, and aromatic intensity render Marrone cv fruits suitable for both the fresh market and candying, i.e., marron glacé. Whereas, sweet chestnut samples, due to their characteristics, have the potential to be used for secondary food products, such as jam, mash chestnut, and flour. The research lays the foundations for a superior data fusion approach for chestnut identification in terms of classification sensitivity and specificity, in which sensory and spectral approaches compensate each other's drawbacks, synergistically contributing to an excellent result.

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.

A high-throughput and machine learning resistance monitoring system to determine the point of resistance for Escherichia coli with tetracycline: Combining UV-visible spectrophotometry with principal component analysis

UV-visible spectroscopy (UV-Vis) is routinely used in microbiology as a tool to check the optical density (OD) pertaining to the growth stages of microbial cultures at the single wavelength of 600 nm, better known as the OD₆₀₀ . Typically, modern UV-Vis spectrophotometers can scan in the region of approximately 200-1000 nm in the electromagnetic spectrum, where users do not extend the use of the instrument's full capability in a laboratory. In this study, the full potential of UV-Vis spectrophotometry (multiwavelength collection) was used to examine bacterial growth phases when treated with antibiotics showcasing the ability to understand the point of resistance when an antibiotic is introduced into the media and therefore understand the biochemical changes of the infectious pathogens. A multiplate reader demonstrated a high throughput experiment (96 samples) to understand the growth of Escherichia coli when varied concentrations of the antibiotic tetracycline was added into the well plates. Principal component analysis (PCA) and partial least squares discriminant analysis were then used as the data mining techniques to interpret the UV-Vis spectral data and generate machine learning "proof of principle" for the UV-Vis spectrophotometer plate reader. Results from this study showed that the PCA analysis provides an accurate yet simple visual classification and the recognition of E. coli samples belonging to each treatment. These data show significant advantages when compared to the traditional OD₆₀₀ method where we can now understand biochemical changes in the system rather than a mere optical density measurement. Due to the unique experimental setup and procedure that involves indirect use of antibiotics, the same test could be used for obtaining practical information on the type, resistance, and dose of antibiotic necessary to establish the optimum diagnosis, treatment, and decontamination strategies for pathogenic and antibiotic resistant species.

Challenges and opportunities of the fourth revolution: a brief insight into the future of food

By 2050, the global population is projected to be in excess of nine billion people. This will result in an increased burden and stress on the food production systems, particularly in adjustments to several stages of the value chain that will require improvements and/or modifications in their effectiveness such as reducing waste, adapting to climate change, food security, and health. Disruptions such as digital agriculture, digital food, food agility, big data, have been utilized to characterize the changes in the way agro-food systems evolve and function, as well as in the approach they have been analyzed, measured, and monitored. It has been long recognized that the food industry is considered as a data driven enterprise. These characteristics are very important as the food industry becomes global and sustainable. The food industry is currently undergoing significant changes, and with this, challenges are occurring. These challenges are brought about from the food chains, climate changes, and the ability to be resilient in the production of food. Furthermore, health and cultural changes to food are occurring, where the diseases of obesity, diabetes, and aging in the population will continue to change the consumer's patterns and choices; whereby the consumer will be persuaded to choose and eat healthy and more nutritious foods. Indeed, the cultural awareness and social innovation to prevent food waste and therefore improve food security and sustainability will also prove to further complexities. This short review will briefly discuss some of the forefront issues in food value chains with a focus on using technology.

Picking Your Brains: Where and How Neuroscience Tools Can Enhance Marketing Research

The use of neuroscience tools to study consumer behavior and the decision making process in marketing has improved our understanding of cognitive, neuronal, and emotional mechanisms related to marketing-relevant behavior. However, knowledge about neuroscience tools that are used in consumer neuroscience research is scattered. In this article, we present the results of a literature review that aims to provide an overview of the available consumer neuroscience tools and classifies them according to their characteristics. We analyse a total of 219 full-texts in the area of consumer neuroscience. Our findings suggest that there are seven tools that are currently used in consumer neuroscience research. In particular, electroencephalography (EEG) and eye tracking (ET) are the most commonly used tools in the field. We also find that consumer neuroscience tools are used to study consumer preferences and behaviors in different marketing domains such as advertising, branding, online experience, pricing, product development and product experience. Finally, we identify two ready-to-use platforms, namely iMotions and GRAIL that can help in integrating the measurements of different consumer neuroscience tools simultaneously. Measuring brain activity and physiological responses on a common platform could help by (1) reducing time and costs for experiments and (2) linking cognitive and emotional aspects with neuronal processes. Overall, this article provides relevant input in setting directions for future research and for business applications in consumer neuroscience. We hope that this study will provide help to researchers and practitioners in identifying available, non-invasive and useful tools to study consumer behavior.

Towards superior plant-based foods using metabolomics

Metabolomics is proving a useful approach for many of the main future goals in agronomy and food production such as sustainability/crop resilience, food quality, safety, storage, and nutrition. Targeted and/or untargeted small-molecule analysis, coupled to chemometric analysis, has already unveiled a great deal of the complexity of plant-based foods, but there is still 'dark matter' to be discovered. Moreover, state-of-the-art food metabolomics offers insights into the molecular mechanisms underlying sensorial and nutritional characteristics of foods and thus enables higher precision and speed. This review describes recent applications of food metabolomics from fork to farm and focuses on the opportunities these bring to continue food innovation and support the shift to plant-based foods.

New frozen product development from strawberries (Fragaria Ananassa Duch.)

Strawberry fruit has a short shelf life. If stored at ambient temperature only lasts 1 day, so it needs to be dried into a frozen product so that its shelf life is longer. Frozen products are favored by consumers because they still have properties like fresh fruit. This study was aimed at examining the physical and sensory characteristics of new frozen products from strawberries. The research sample was freeze-dried at 3 variations of the heating plate temperature were 40, 50, and 60 °C and 3 variations of the drying time were 24, 36, and 48 h. The research parameters observed were weight loss, water content, texture, color, aroma, and taste. The results showed that the freeze-vacuum drying process has a significant influence on the parameters of weight loss, moisture content, texture, and color of frozen strawberries, but does not influence significantly to aroma and taste. The highest weight loss and evaporation were obtained at 60 °C and 48 h of drying time. Frozen strawberries most preferred by panelists are those that are freeze-dried at 50 °C and a drying time of 36 h because they have aroma and flavor that seem fresh strawberries.

Physicochemical Fingerprint of "Pera Rocha do Oeste". A PDO Pear Native from Portugal

"Pera Rocha do Oeste" is a pear (Pyrus communis L.) variety native from Portugal with a Protected Designation of Origin (PDO). To supply the world market for almost all the year, the fruits are kept under controlled storage. This study aims to identify which classical physicochemical parameters (colour, total soluble solids (TSS), pH, acidity, ripening index, firmness, vitamin C, total phenols, protein, lipids, fibre, ash, other compounds including carbohydrates, and energy) could be fingerprint markers of PDO "Pera Rocha do Oeste". For this purpose, a data set constituting fruits from the same size, harvested from three orchards of the most representative PDO locations and stored in refrigerated conditions for 2 or 5 months at atmospheric conditions or for 5 months under a modified atmosphere, were selected. To validate the fingerprint parameters selected with the first set, an external data set was used with pears from five PDO orchards stored under different refrigerated conditions. Near infrared (NIR) spectroscopy was used as a complementary tool to assess the global variability of the samples. The lightness of the pulp; the b* CIELab coordinate of the pulp and peel; and the pulp TSS, pH, firmness, and total phenols, due to their lower variability, are proposed as fingerprint markers of this pear.