Design of experiments and regression modelling in food flavour and sensory analysis: A review

Endogenous storage proteins influence Rice flavor: Insights from protein-flavor correlations and predictive modeling

This study investigated the correlation between endogenous storage proteins and aromatic compounds in rice, and their collective influence on rice eating quality. Six rice samples, varying in four endogenous storage proteins through gene editing genetically modified, were analyzed for their sensory characteristics and volatile compounds utilizing GC-E-nose, GC-MS, GC-MS-O, texture analyzer, and sensory evaluation. The results indicated that a total of 55 flavor compounds were identified, with 2-acetyl-1-pyrroline identified as the key aroma compound, positively correlated with prolamin content, while negatively correlated with glutelin and albumin. The concentrations of glutelin and prolamin significantly influence the odor, taste, and texture of rice. Additionally, six prediction models were evaluated, with the optimal Support Vector Regression (SVR) model selected for predicting rice flavor profiles based on protein content. This study provides a foundation for understanding key factors in rice aroma and texture, offering valuable guidance for gene-editing strategies aimed at enhancing rice flavor.

Au@Ag hollow nanoshells-based SERS integrated microfluidic chip as a sample-to-answer platform for the ultra-sensitive detection of geosmin

BACKGROUND

Geosmin (GSM) can compromise the immune systems of aquatic organisms, rendering them more vulnerable to viral and bacterial infections, thereby adversely affecting their growth, reproduction, yield, and quality. Given the relatively low odor thresholds of GSM, there is a critical demand for the development of a highly sensitive and rapid detection method. According to the principle of Surface-enhanced Raman spectroscopy (SERS), localized surface plasmon resonance (LSPR) greatly enhances the Raman signals of adsorbed molecules. To date, no study has reported the application of SERS to detect GSM.

RESULTS

Dual-metal nanomaterials with hollow structures have been proven to provide a large surface area and heightened localized surface plasmon resonance, thereby enhancing the sensitivity of Raman signals. In this study, a sample-to-answer platform was constructed by integrating Au@Ag hollow nanoshells (HNSs)-based SERS and a microfluidic chip for the sensitive, fast, and direct determination of GSM. Under 532 nm excitation, GSM exhibit Raman peaks on the SERS-active Au@Ag HNSs, and there is a relationship between the peak intensity and the concentration of GSM. Owing to the integration of the microfluidic chip, only microliters of reagent are required, and the test results can be achieved within 4 min. The constructed sample-to-answer platform showed a good linear response to GSM in the range of 1 ng/L-1 mg/L, with a detection limit of 0.16 ng/L. An optimal calibration model is established by combining stoichiometric algorithms.

SIGNIFICANCE

This work realized the ultra-highly sensitive and fast determination of GSM based on SERS-active Au@Ag HNSs, which provides new insights and is very promising for the on-site monitoring of earthy odors. Our study makes a significant contribution to the literature because the developed SERS-based detection method does not require labels or biomaterials, providing the possibility of highly sensitive and on-site testing of contaminants in food and the environment.

Unlocking DOE potential by selecting the most appropriate design for rAAV optimization

Producing recombinant adeno-associated virus (rAAV) for gene therapy via triple transfection is an intricate process involving many cellular interactions. Each of the different elements encoded in the three required plasmids-pHelper, pRepCap, and pGOI-plays a distinct role, affecting different cellular pathways when producing rAAVs. The required expression balance emphasizes the critical need to fine-tune the concentration of all these different elements. The use of design of experiments (DOE) to find optimal ratios is a powerful method to streamline the process. However, the choice of the DOE method and design construction is crucial to avoid misleading results. In this work, we examined and compared four distinct DOE approaches: rotatable central composite design (RCCD), Box-Behnken design (BBD), face-centered central composite design (FCCD), and mixture design (MD). We compared the abilities of the different models to predict optimal ratios and interactions among the plasmids and the transfection reagent. Our findings revealed that blocking is essential to reduce the variability caused by uncontrolled random effects and that MD coupled with FCCD outperformed all other approaches, improving volumetric productivity 109-fold. These outcomes underscore the importance of selecting a model that can effectively account for the biological context, ultimately yielding superior results in optimizing rAAV production.

The eating rate of bread predicted from its sensory texture and physical properties

Eating rate (ER) can moderate energy intake and ER can be modified by the texture and physical properties of food. However, the magnitude of the effects is not well known. The aim of this study was to investigate how bread texture and physical properties determine ER. In a randomised crossover study, 36 healthy participants (age: 25 ± 6 years, BMI: 22 ± 2 kg m-2) consumed nine different bread types. Video coding was used to characterise oral processing behaviour. Sensory texture was evaluated on visual analogue scales. Physical properties were measured using texture profile analysis, puncture tests, geometrical and water-related measures. Two models were developed using response surface methodology (RSM) that predict the ER based on sensory and physical properties. The results showed from slow to fast ER: bread slices < hard buns < soft buns. The slowest bread type (wholemeal bread slice) was consumed 40% slower than the fastest bread type (soft white bun) (P < 0.001), explained by smaller bite sizes and more chews. For the sensory texture, ER was positively correlated with crumb adhesiveness and negatively correlated with crumb dryness. For the physical properties, ER was positively correlated with height and volume, and negatively with crumb cohesiveness and crust hardness. The models based on physical properties (R2 = 0.91) and sensory texture (R2 = 0.89) were both able to estimate ER, but the model based on physical properties performed slightly better. The insights from the relationships from the sensory and physical measures can both be used to modify the texture of breads, to effectively decrease ER and eventually help to prevent overconsumption.

Bioactive Compounds of Green Phenolic Extracts Obtained via Microwave-Assisted Extraction of Sideritis Species Grown in Greece

The purpose of the present study was to compare the polyphenolic compounds extracted from five Sideritis species grown in Greece; S. scardica, S. clandestina, S. raeseri, S. euboea, and S. syriaca, using the Microwave-Assisted Extraction (MAE) process. To maximize the extraction yield (EY), total phenolic compounds (TPC), hypolaetin (HYP) and isoscutellarein (ISC), derivative contents (target phenolics), the response surface methodology was used for S. scardica. A Box-Behnken design was undertaken to study the effect of ethanol concentration (30-100%), extraction temperature (40-100 °C), and extraction time (5-25 min) on the responses. The optimal MAE parameters were 87.9% (v/v) ethanol, 25 min, and 100 °C. Under these conditions, there was a good agreement between experimental and predicted values, indicating the reliability of the predictions for Sideritis extracts. Phenolic compounds were then extracted under these conditions, from the five Sideritis species under investigation. The TPC, total flavonoid content (TFC), antioxidant activity based on DPPH, ABTS, and FRAP assays as well as the phenolic profile of different Sideritis extracts, evaluated via HPLC-DAD-MS, were compared. A similar phenolic profile was observed among the five Sideritis species, with HYP and ISC derivatives showing variations in their contents as a function of Sideritis species. MAE Sideritis extracts could be considered green and natural antioxidants for medicinal, cosmetic, and food purposes, accompanied by sustainable approaches.

Research Progress of Machine Learning in Extending and Regulating the Shelf Life of Fruits and Vegetables

Fruits and vegetables are valued for their flavor and high nutritional content, but their perishability and seasonality present challenges for storage and marketing. To address these, it is essential to accurately monitor their quality and predict shelf life. Unlike traditional methods, machine learning efficiently handles large datasets, identifies complex patterns, and builds predictive models to estimate food shelf life. These models can be continuously refined with new data, improving accuracy and robustness over time. This article discusses key machine learning methods for predicting shelf life and quality control of fruits and vegetables, with a focus on storage conditions, physicochemical properties, and non-destructive testing. It emphasizes advances such as dataset expansion, model optimization, multi-model fusion, and integration of deep learning and non-destructive testing. These developments aim to reduce resource waste, provide theoretical basis and technical guidance for the formation of modern intelligent agricultural supply chains, promote sustainable green development of the food industry, and foster interdisciplinary integration in the field of artificial intelligence.

Iterative experimental design and identifiability analysis of composite material failure models

The parameter identification of failure models for composite plies can be cumbersome, due to multiple effects as the consequence of brittle fracture. Our work proposes an iterative, nonlinear design of experiments (DoE) approach that finds the most informative experimental data to identify the parameters of the Tsai-Wu, Tsai-Hill, Hoffman, Hashin, max stress and Puck failure models. Depending on the data, the models perform differently, therefore, the parameter identification is validated by the Euclidean distance of the measured points to the closest ones on the nominal surface. The resulting errors provide a base for the ranking of the models, which helps to select the best fitting. Following the validation, the sensitivity of the best model is calculated by partial differentiation, and a theoretical surface is generated. Lastly, an iterative design of the experiments is implemented to select the optimal set of experiments from which the parameters can be identified from the least data by minimizing the fitting error. In this way, the number of experiments required for the identification of a model of a composite material can be significantly reduced. We demonstrate how the proposed method selected the most optimal experiments out of generated data. The results indicate that if the dataset contains enough information, the method is robust and accurate. If the data set lacks the necessary information, novel material tests can be proposed based on the optimal points of the parameters' sensitivity of the generated failure model surface.

Advancing Mycotoxin Detection in Food and Feed: Novel Insights from Surface-Enhanced Raman Spectroscopy (SERS)

The implementation of low-cost and rapid technologies for the on-site detection of mycotoxin-contaminated crops is a promising solution to address the growing concerns of the agri-food industry. Recently, there have been significant developments in surface-enhanced Raman spectroscopy (SERS) for the direct detection of mycotoxins in food and feed. This review provides an overview of the most recent advancements in the utilization of SERS through the successful fabrication of novel nanostructured materials. Various bottom-up and top-down approaches have demonstrated their potential in improving sensitivity, while many applications exploit the immobilization of recognition elements and molecular imprinted polymers (MIPs) to enhance specificity and reproducibility in complex matrices. Therefore, the design and fabrication of nanomaterials is of utmost importance and are presented herein. This paper uncovers that limited studies establish detection limits or conduct validation using naturally contaminated samples. One decade on, SERS is still lacking significant progress and there is a disconnect between the technology, the European regulatory limits, and the intended end-user. Ongoing challenges and potential solutions are discussed including nanofabrication, molecular binders, and data analytics. Recommendations to assay design, portability, and substrate stability are made to help improve the potential and feasibility of SERS for future on-site agri-food applications.

E-nose and colorimetric sensor array combining homologous data fusion strategy discriminating the roasting degree of large-leaf yellow tea

Different degrees of roasting result in differences in the quality and flavor of large-leaf yellow tea. The current sensory evaluation and chemical detection methods cannot meet the requirement of online differentiation of LYT roasting degree, so an accurate and comprehensive assessment method needs to be developed urgently. First, the two aroma sensing technologies were compared. Two variable screening methods and three recognition algorithms were employed to build discriminant models. The results showed that the discrimination rate of the colorimetric sensor array (CSA) in the prediction set reached 91.89 %, outperforming that of the E-nose. Subsequently, three fusion strategies were applied to improve the discrimination accuracy. The discrimination rate of the middle fusion strategy resulted in an optimal resolution of 94.59 %. The results obtained from the homologous fusion were able to evaluate the roasting degree comprehensively and accurately, which provides a new method and idea for tea aroma quality.

Predicting and improving complex beer flavor through machine learning

The perception and appreciation of food flavor depends on many interacting chemical compounds and external factors, and therefore proves challenging to understand and predict. Here, we combine extensive chemical and sensory analyses of 250 different beers to train machine learning models that allow predicting flavor and consumer appreciation. For each beer, we measure over 200 chemical properties, perform quantitative descriptive sensory analysis with a trained tasting panel and map data from over 180,000 consumer reviews to train 10 different machine learning models. The best-performing algorithm, Gradient Boosting, yields models that significantly outperform predictions based on conventional statistics and accurately predict complex food features and consumer appreciation from chemical profiles. Model dissection allows identifying specific and unexpected compounds as drivers of beer flavor and appreciation. Adding these compounds results in variants of commercial alcoholic and non-alcoholic beers with improved consumer appreciation. Together, our study reveals how big data and machine learning uncover complex links between food chemistry, flavor and consumer perception, and lays the foundation to develop novel, tailored foods with superior flavors.

Correlating instrumental measurements and sensory perceptions of foods with different textural properties for people with impaired oral and swallowing capabilities - A review

The rising global life expectancy has underlined the necessity of designing novel and tasty food products, suitable for seniors and people with impaired oral and swallowing functions. For developing these products, texture should be optimised from rheological, colloidal, tribological, and masticatory points of view. The current review provides an overview of different studies based on shear rheological, tribological, and in vitro mastication properties of model or real food systems intended for the elderly and/or people with swallowing dysfunctions, with special emphasis on the relation between the instrumental measurements and sensory perceptions of foods. Several works demonstrated that instrumental data from shear rheological and tribological tests complement the sensory evaluations of foods, providing useful information when designing food commodities for specific populations. Conversely, only few works correlated the instrumental data obtained from artificial mouths and/or simulated masticators with the sensory attributes generated by trained assessors. Broaden knowledge of these topics will help in formulating and adapting foods with enhanced functionalities for people with impaired oral and swallowing capabilities. Shear rheology, soft oral tribology, and simulated mastication tests are crucial in designing safe- and easy-swallowing food products.

Flavor profile and role of macromolecules in the flavor generation of shrimp meat and valorization of shrimp by-products as a source of flavor compounds: a review

Shrimps are a widely cultivated species among crustaceans worldwide due to their nutritional profile and delicacy. Because of their unique flavor, shrimp-based food products are gaining consumer demand, so there is a need to understand the flavor chemistry of shrimp meat. Further, the processing and macromolecules of shrimp meat play a significant role in flavor generation and suggest a focus on their research. However, shrimp processing generates a large amount of solid and liquid waste, creating disposal problems and environmental hazards. To overcome this, utilizing these waste products, a rich source of valuable flavor compounds is necessary. This review comprehensively discusses the nutritional aspects, flavor profile, and role of macromolecules in the flavor generation of shrimp meat. Besides, recent trends in analyzing the aroma profile of shrimp and the benefits of shrimp by-products as a source of flavor compounds have been addressed. The delicious flavor of shrimp meat is due to its volatile and nonvolatile flavor compounds. Proteins play a major role in the textural and flavor adsorption properties of shrimp meat-based products. Green extraction technologies, especially ultrasonication, are recommended for valorizing shrimp by-products as a source of flavor compounds, which have enormous applications in the food and flavor industries.

Transforming the fermented fish landscape: Microbiota enable novel, safe, flavorful, and healthy products for modern consumers

Regular consumption of fish promotes sustainable health while reducing negative environmental impacts. Fermentation has long been used for preserving perishable foods, including fish. Fermented fish products are popular consumer foods of historical and cultural significance owing to their abundant essential nutrients and distinct flavor. This review discusses the recent scientific progress on fermented fish, especially the involved flavor formation processes, microbial metabolic activities, and interconnected biochemical pathways (e.g., enzymatic/non-enzymatic reactions associated with lipids, proteins, and their interactions). The multiple roles of fermentation in preservation of fish, development of desirable flavors, and production of health-promoting nutrients and bioactive substances are also discussed. Finally, prospects for further studies on fermented fish are proposed, including the need of monitoring microorganisms, along with the precise control of a fermentation process to transform the traditional fermented fish to novel, flavorful, healthy, and affordable products for modern consumers. Microbial-enabled innovative fermented fish products that consider both flavor and health benefits are expected to become a significant segment in global food markets. The integration of multi-omics technologies, biotechnology-based approaches (including synthetic biology and metabolic engineering) and sensory and consumer sciences, is crucial for technological innovations related to fermented fish. The findings of this review will provide guidance on future development of new or improved fermented fish products through regulating microbial metabolic processes and enzymatic activities.

Treatment of Pulp Black Liquor with an Ionic Liquid: Modeling and Optimization by Using Response Surface Methodology and Central Composite Design

In this study, selective extraction towards ionic liquid trihexyltetradecylphosphonium chloride ([P₆₆₆₁₄ ]Cl) of lignin residues, polysaccharides and organic acids present in black liquor (BL), the main principal wastewater of pulping industry was studied. With the objective of finding optimized conditions allowing to extract lignin residues while polysaccharides and organic acids remain in aqueous solution, a design of experiments approach based on a response surface methodology was used. Three continuous factors, namely initial pH varying from 9 to 13.5, dilution of BL varying from 5 to 20 and volumetric ratio of black liquor vs. ionic liquid R_V ., varying from 1 to 19, were investigated. Concentration of lignin residues, polysaccharides and organic acids were measured using Folin-Ciocalteu method, the anthrone method and HPLC, respectively. Results showed that a multi-response optimization led to the extraction of 84.8 % of lignin residues, 66.0 % of polysaccharides, and no extraction of OA under optimised conditions.

Predicting the Optimal Input Parameters for the Desired Print Quality Using Machine Learning

3D printing is a growing technology being incorporated into almost every industry. Although it has obvious advantages, such as precision and less fabrication time, it has many shortcomings. Although several attempts were made to monitor the errors, many have not been able to thoroughly address them, like stringing, over-extrusion, layer shifting, and overheating. This paper proposes a study using machine learning to identify the optimal process parameters such as infill structure and density, material (ABS, PLA, Nylon, PVA, and PETG), wall and layer thickness, count, and temperature. The result thus obtained was used to train a machine learning algorithm. Four different network architectures (CNN, Resnet152, MobileNet, and Inception V3) were used to build the algorithm. The algorithm was able to predict the parameters for a given requirement. It was also able to detect any errors. The algorithm was trained to pause the print immediately in case of a mistake. Upon comparison, it was found that the algorithm built with Inception V3 achieved the best accuracy of 97%. The applications include saving the material from being wasted due to print time errors in the manufacturing industry.

A Comparison between the Egg Yolk Flavor of Indigenous 2 Breeds and Commercial Laying Hens Based on Sensory Evaluation, Artificial Sensors, and GC-MS

The focus of this study was to compare the yolk flavor of eggs from laying hens of Chinese indigenous and commercial, based on detection of volatile compounds, fatty acids, and texture characteristics determination, using sensory evaluation, artificial sensors (electronic nose (E-nose), electronic tongue (E-tongue)), and gas chromatography-mass spectrometry (GC-MS). A total of 405 laying hens (Hy-Line Brown (n = 135), Xueyu White (n = 135), and Xinyang Blue (n = 135)) were used for the study, and 540 eggs (180 per breed) were collected within 48 h of being laid and used for sensory evaluation and the instrument detection of yolk flavor. Our research findings demonstrated significant breed differences for sensory attributes of egg yolk, based on sensory evaluation and instrument detection. The milky flavor, moisture, and compactness scores (p < 0.05) of egg yolk from Xueyu White and Xinyang Blue were significantly higher than that of Hy-Line Brown. The aroma preference scores of Xinyang Blue (p < 0.05) were significantly higher, compared to Hy-Line Brown and Xueyu White. The sensor responses of WIW and W2W from E-nose and STS from E-tongue analysis were significantly higher foe egg yolks of Hy-Line Brown (p < 0.05), compared to that of Xueyu White and Xinyang Blue. Additionally, the sensor responses of umami from E-tongue analysis, was significantly higher for egg yolks of Xueyu White (p < 0.05), compared to that of Hy-Line Brown and Xinyang Blue. Besides, the contents of alcohol and fatty acids, such as palmitic acid, oleic acid, and arachidonic acid, in egg yolk were positively correlated with egg flavor. The texture analyzer showed that springiness, gumminess, and hardness of Hy-Line Brown and Xueyu White (p < 0.05) were significantly higher, compared to Xinyang Blue. The above findings demonstrate that the egg yolk from Chinese indigenous strain had better milky flavor, moisture, and compactness, as well as better texture. The egg yolk flavors were mainly due to presence of alcohol and fatty acids, such as palmitic acid, oleic acid, and arachidonic acid, which would provide research direction on improvement in egg yolk flavor by nutrition. The current findings validate the strong correlation between the results of egg yolk flavor and texture, based on sensory evaluation, artificial sensors, and GC-MS. All these indicators would be beneficial for increased preference for egg yolk flavor by consumers and utilization by food processing industry, as well as a basis for the discrimination of eggs from different breeds of laying hens.

An Overview on the Application of Chemometrics Tools in Food Authenticity and Traceability

The use of advanced chemometrics tools in food authenticity research is crucial for managing the huge amount of data that is generated by applying state-of-the-art analytical methods such as chromatographic, spectroscopic, and non-targeted fingerprinting approaches. Thus, this review article provides description, classification, and comparison of the most important statistical techniques that are commonly employed in food authentication and traceability, including methods for exploratory data analysis, discrimination, and classification, as well as for regression and prediction. This literature revision is not intended to be exhaustive, but rather to provide a general overview to non-expert readers in the use of chemometrics in food science. Overall, the available literature suggests that the selection of the most appropriate statistical technique is dependent on the characteristics of the data matrix, but combining complementary tools is usually needed for properly handling data complexity. In that way, chemometrics has become a powerful ally in facilitating the detection of frauds and ensuring the authenticity and traceability of foods.

E-nose, E-tongue Combined with GC-IMS to Analyze the Influence of Key Additives during Processing on the Flavor of Infant Formula

In order to analyze the influence of key additives during processing on the flavor of infant formula, the headspace-gas chromatography-ion mobility spectrometry, electronic tongue, and electronic nose techniques were used to evaluate flavor during the processing of stage 1 infant formula milk powder (0-6 months), including the analysis of seven critical additives. A total of 41 volatile compounds were identified, involving 12 aldehydes, 11 ketones, 9 esters, 4 olefins, 2 alcohols, 2 furans, and 1 acid. The electronic nose metal oxide sensor W5S had the highest response, followed by W1S and W2S, illustrating that these three sensors had great effects on distinguishing samples. The response results of the electronic tongue showed that the three sensory attributes of bitter, salty, and umami, as well as the richness of aftertaste, were more prominent, which contributed significantly to evaluating the taste profile and distinguishing among samples. Raw milk is an essential control point in the flavor formation process of stage 1 infant formula milk powder. Demineralized whey powder is the primary source of potential off-flavor components in hydrolyzed milk protein infant formula. This study revealed the quality characteristics and flavor differences of key additives in the production process of stage 1 infant formula milk powder, which could provide theoretical guidance for the quality control and sensory improvement of the industrialized production of infant formula.

Design of experiment techniques for the optimization of chromatographic analysis conditions: A review

Design of experiment (DoE) techniques have been widely used in the field of chromatographic parameters optimization as a valuable tool. A systematic literature review of the available DoE techniques applied to the development of a chromatographic analysis method is presented in this paper. First, the most common available designs and the implementation steps of DoE are comprehensively introduced. Then the studies in recent 10 years for the application of DoE techniques in various chromatographic techniques are discussed, such as capillary electrophoresis, liquid chromatography, gas chromatography, thin-layer chromatography, and high-speed countercurrent chromatography. Current problems and future outlooks are finally given to provide a certain inspiration of research in the application of DoE techniques to the different chromatographic techniques field. This review contributes to a better understanding of the DoE techniques for the efficient optimization of chromatographic analysis conditions, especially for the analysis of complex systems, such as multicomponent drugs and natural products.

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.

Optimising process and product performance in complex systems: a study in the automotive industry

Purpose

This study responds to calls from industry and the literature to cope with the enormous challenges faced by companies operating in competitive business sectors. The main objective of this paper is to investigate how managers can optimise product quality and process efficiency of complex systems.

Design/methodology/approach

In this paper, a design of experiments (DoE) method was used to improve the development of complex products and manufacturing processes in the industry of automotive audio components. To identify the optimal combination that minimises quality problems occurring with subwoofer speakers in the marketing, this study proposed a full Factorial experiment 24 with three replications in a single block summarised in an analysis of the interaction among the factors.

Findings

The research findings revealed the factors and levels regarding both the product development and manufacturing processes that significantly impact the quality and reliability performance of the subwoofer speaker analysed. The findings from the article allowed the company to prioritise internal improvements to enhance product quality and process efficiency. Other automotive firms will benefit from the research findings obtained.

Practical implications

From a managerial perspective, this research presented the DoE methodology as a real opportunity to deal with the inherent complexity of the manufacturing process in the automotive audio components sector. This research assist managers with insights into how they can improve the quality performance in production systems and in the market.

Originality/value

This study is an original contribution to the advance of theory and empirical implementation of DoE in competitive industrial sectors.

Chemometric and sensometric techniques in enological data analysis

Enological evaluations capture the chemical and sensory space of wine using different techniques; many sensory methods as well as a variety of analytical chemistry techniques contribute to the amount of information generated. Data fusion, especially integrating data sets, is important when working with complex systems. The success reported when trying to integrate different modalities is generally low and has been attributed to the lack of statistically considerate strategies focusing on the data handling process. Multiple stages of data handling must be carefully considered when dealing with multi-modal data. In this review, the different stages in the data analysis process were examined. The study revealed misconceptions surrounding the process and elucidated rules for purpose-driven approaches by examining the complexities of each stage and the impact the decisions made at each stage have on the resulting models. The two major modeling approaches are either supervised (discrimination, classification, prediction) or unsupervised (exploration). Supervised approaches were emphatic on the pre-processing steps and prioritized increasing performance. Unsupervised approaches were mostly used for preliminary steps. The review found aspects often neglected when it came to the data collection and capturing which in the end contributed to the low success in combining sensory and chemistry data.

Flavor impartment of mushroom on egg whites and sensory properties of egg whites with mushroom topping using quantitative descriptive analysis

BACKGROUND

Mushroom possesses desirable aroma, taste, texture, health-promoting and disease-preventing dietary components, making it an ideal ingredient, especially for animal-based food substitution. Nevertheless, no study has replaced egg whites partially with mushrooms and investigated their sensory quality. This study aimed to investigate flavor impartment of mushroom on egg whites and the sensory quality of roasting and steaming egg whites replaced by white and crimini mushrooms at 0%, 10%, 20% and 30%, respectively, using a panel trained with aroma chemical references for 31 sensory attributes with 0-10 line scales.

RESULTS

Roasted and steamed egg whites possessed major sensory attributes of sulfury and egg-like aroma and flavor (intensities > 3). After mushroom topping was added, the dominant sensory attributes shifted to mushroom-based flavor characteristics, including mushroom-like, earthy, dark meat, roasted, hay, soybean, potato, woody, umami, bitter, astringent and firmness texture. Mushroom variety showed significant (P ≤ 0.05) impacts on egg white sensory quality, with crimini introducing more intense flavor. The higher the mushroom proportion with egg whites, the more intense was the flavor associated with mushroom. Mushroom could enhance egg-like flavor in multiple dimensions, including aroma, taste and texture, according to partial least square regression.

CONCLUSION

White and crimini mushrooms enriched roasted and steamed egg white sensory quality with introduction of characteristic sensory attributes from mushrooms. Mushroom variety and proportion with egg whites displayed significant impacts on egg white sensory properties. The study contributed to understanding the impact of mushrooms on egg white sensory profile and served as a guide in incorporating mushroom in product development. © 2021 Society of Chemical Industry.

Quality by Design tools reducing the gap from bench to bedside for nanomedicine

Pharmaceutical nanotechnology research is focused on smart nano-vehicles, which can deliver active pharmaceutical ingredients to enhance their efficacy through any route of administration and in the most varied therapeutical application. The design and development of new nanopharmaceuticals can be very laborious. In recent years, the application of mathematics, statistics and computational tools is emerging as a convenient strategy for this purpose. The application of Quality by Design (QbD) tools has been introduced to guarantee quality for pharmaceutical products and improve translational research from the laboratory bench into applicable therapeutics. In this review, a collection of basic-concept, historical overview and application of QbD in nanomedicine are discussed. A specific focus has been put on Response Surface Methodology and Artificial Neural Network approaches in general terms and their application in the development of nanomedicine to monitor the process parameters obtaining optimized system ensuring its quality profile.

Rapid Assessment of Quality Changes in French Fries during Deep-frying Based on FTIR Spectroscopy Combined with Artificial Neural Network

Fourier transform infrared (FTIR) spectroscopy combined with backpropagation artificial neural network (BP-ANN) were utilized for rapid and simultaneous assessment of the lipid oxidation indices in French fries. The conventional indexes (i.e. total polar compounds, oxidized triacylglycerol polymerized products, oxidized triacylglycerol monomers, triacylglycerol hydrolysis products, and acid value), and FTIR absorbance intensity in French fries were determined during the deep-frying process, and the results showed the French fries had better quality in palm oil, followed by sunflower oil, rapeseed oil and soybean oil. The FTIR spectra of oil extracted from French fries were correlated to the reference oxidation indexes determined by AOCS standard methods. The results of BP-ANN prediction showed that the model based on FTIR fitted well (R² > 0.926, RMSEC < 0.481) compared with partial least-squares model (R² > 0.876). This facile strategy with excellent performance has great potential for rapid characterization quality of French fries during frying.

E-Nose and Olfactory Assessment: Teamwork or a Challenge to the Last Data? The Case of Virgin Olive Oil Stability and Shelf Life

Electronic nose (E-nose) devices represent one of the most trailblazing innovations in current technological research, since mimicking the functioning of the biological sense of smell has always represented a fascinating challenge for technological development applied to life sciences and beyond. Sensor array tools are right now used in a plethora of applications, including, but not limited to, (bio-)medical, environmental, and food industry related. In particular, the food industry has seen a significant rise in the application of technological tools for determining the quality of edibles, progressively replacing human panelists, therefore changing the whole quality control chain in the field. To this end, the present review, conducted on PubMed, Science Direct and Web of Science, screening papers published between January 2010 and May 2021, sought to investigate the current trends in the usage of human panels and sensorized tools (E-nose and similar) in the food industry, comparing the performances between the two different approaches. In particular, the focus was mainly addressed towards the stability and shelf life assessment of olive oil, the main constituent of the renowned “Mediterranean diet”, and nowadays appreciated in cuisines from all around the world. The obtained results demonstrate that, despite the satisfying performances of both approaches, the best strategy merges the potentialities of human sensory panels and technological sensor arrays, (i.e., E-nose somewhat supported by E-tongue and/or E-eye). The current investigation can be used as a reference for future guidance towards the choice between human panelists and sensorized tools, to the benefit of food manufacturers.

Quality control of fresh strawberries by a random forest model

BACKGROUND

Strawberry quality is one of the most important factors that guarantees consistent commercialization of the fruit and ensures the consumer's satisfaction. This work makes innovative use of random forest (RF) to predict sensory measures of strawberries using physical and physical-chemical variables. Furthermore, it also employs these same physical and physical-chemical variables to classify strawberries in the classes "satisfied" or "not satisfied" and "would pay more" or "wouldn't pay more. The RF-based model predicts the acceptance, expectation, ideal of sweetness, ideal of acidity, and the ideal of succulence based on the physical and physical-chemical data. Then, the predicted parameters are used as input for the RF-based classification model.

RESULTS

The RF achieved a coefficient of determination R²  > 0.72 and a root-mean-squared error (RMSE) smaller than 0.17 for the prediction task, which indicates that one can estimate the sensory measures of strawberries using physical and physical-chemical data. Furthermore, the RF was able to classify 87.95% of the strawberry samples correctly into the classes 'satisfied' and 'not satisfied' and 78.99% in the classes 'would pay more' or 'would not pay more'. A two-step RF model, which employed both physical and physical-chemical data to classify strawberry samples regarding the consumer's response also correctly classified 100% and 90.32% of the samples with respect to consumers' satisfaction and their willingness to pay more, respectively.

CONCLUSION

The results indicate that the developed models can be used in the quality control of strawberries, supporting the establishment of quality standards that consider the consumer's response. The proposed methodology can be extended to control the sensory quality of other fruits. © 2021 Society of Chemical Industry.

Evaluating the Effect of a Brewery By-Product as Feed Supplementation on the Quality of Eggs by Means of a Human Panel and E-Tongue and E-Nose Analysis

The objective of our research was to evaluate the possible alteration of the organoleptic properties of eggs produced by hens (Lohmann Brown-Classic) fed with diets containing different doses of an industrial by-product enriched with organic zinc (Zincoppyeast, ZP): Control 0%, ZP 2.5%, and ZP 5.0%. Eggs were collected after 30 days (batch 1) and 60 days (batch 2) of feeding with the experimental diets and subjected to chemical, microbiological, human sensory, e-nose, and e-tongue analyses. There was no significant difference among the microbiological status of eggs of the three groups, but there were significant differences (p < 0.05) in the fat (9.5% vs. 9.3%) and protein contents (12.7% vs. 13.4%) of the Control and ZP 5.0% groups, respectively. Human sensory analysis showed no clear change in the organoleptic characteristics of the eggs. Using linear discriminant analysis (LDA), the e-tongue could recognize the three groups of eggs in batch 1 and batch 2 with 95.9% and 100% accuracy and had a prediction accuracy of 64.8% and 56.2%, respectively. When the eggs were incubating at 50 °C or 80 °C before the e-nose analysis, the groups of eggs could be recognized with 98.0% and 82.7% accuracy, and predicted with 68.5% and 62.2% accuracy, respectively, using principal component analysis-based discriminant analysis (PCA–DA). The aroma compounds and respective sensory descriptors showing changes among the different groups of eggs (batch, storage, and feeding) were identified based on the e-nose analysis. The supplementation of laying hens’ feed with the investigated industrial by-product can be applied without any substantial effect on egg quality, which can, however, be detected with advanced analytical methods.

Stabilization and attributive amelioration of sugarcane juice by naturally derived preservatives using aonla and moringa extract

Sugarcane juice (SCJ) is a cheap, popular, and very nutritious beverage served at roadside stalls in many countries during harvesting season. The juice is normally consumed immediately after extraction as fermentation sets within a few hours of extraction. Preserving the raw sugarcane juice is always challenging because it spoils within a few hours of extraction due to fermentation. Therefore, the bottling, distribution, and marketing of sugarcane are difficult tasks. The present study was designed to investigate the effect of naturally derived preservatives using aonla extract (AE) and moringa extract (ME) in different proportions (0%, 3%, 5%, and 7%) for the preservation of SCJ during 21 days of the storage period at 4 ± 2°C temperature. The effect of extracts and storage time were analyzed on physicochemical parameters, bioactive compounds, enzymatic, microbiological, and sensory analyses of SCJ. A significant improvement in pH of 5% AE (5.30 ± 0.06) and 5% ME (5.36 ± 0.02) was observed at 21 days as compared to control (5.89 ± 0.02). The total phenolic contents in 7% ME were also observed to be retained (4.4 ± 0.02 mg GAE/mL) at 21 days as compared to control (2.65 ± 0.03 mg GAE/mL). Other physicochemical and phytochemical analyses including titratable acidity, total soluble solids, total flavonoids, ascorbic acid, 2,2-Diphenyl-2-picrylhydrazyl (DPPH), and ferric reducing antioxidant power assay (FRAP) also indicated that SCJ treated with ME was significantly stable (p < .05) regarding quality parameters, nutritional and sensory attributes at different storage intervals. These findings may be practical for the large-scale production, storage, and marketing of SCJ products.

Perfume and Flavor Engineering: A Chemical Engineering Perspective

In the last two decades, scientific methodologies for the prediction of the design, performance and classification of fragrance mixtures have been developed at the Laboratory of Separation and Reaction Engineering. This review intends to give an overview of such developments. It all started with the question: what do we smell? The Perfumery Ternary Diagram enables us to determine the dominant odor for each perfume composition. Evaporation and 1D diffusion model is analyzed based on vapor-liquid equilibrium and Fick's law for diffusion giving access to perfume performance parameters. The effect of matrix and skin is addressed and the trail of perfumes analyzed. Classification of perfumes with the perfumery radar is discussed. The methodology is extended to flavor and taste engineering. Finally, future research directions are suggested.

Design of experiments for the optimization of SOFI super-resolution microscopy imaging

Super-resolution optical fluctuation imaging (SOFI) is a well-known super-resolution technique appreciated for its versatility and broad applicability. However, even though an extended theoretical description is available, it is still not fully understood how the interplay between different experimental parameters influences the quality of a SOFI image. We investigated the relationship between five experimental parameters (measurement time, on-time t _on, off-time t _off, probe brightness, and out of focus background) and the quality of the super-resolved images they yielded, expressed as Signal to Noise Ratio (SNR). Empirical relationships were modeled for second- and third-order SOFI using data simulated according to a D-Optimal design of experiments, which is an ad-hoc design built to reduce the experimental load when the total number of trials to be conducted becomes too high for practical applications. This approach proves to be more reliable and efficient for parameter optimization compared to the more classical parameter by parameter approach. Our results indicate that the best image quality is achieved for the fastest emitter blinking (lowest t _on and t _off), lowest background level, and the highest measurement duration, while the brightness variation does not affect the quality in a statistically significant way within the investigated range. However, when the ranges spanned by the parameters are constrained, a different set of optimal conditions may arise. For example, for second-order SOFI, we identified situations in which the increase of t _off can be beneficial to SNR, such as when the measurement duration is long enough. In general, optimal values of t _on and t _off have been found to be highly dependent from each other and from the measurement duration.