Predictions of acidity, soluble solids and firmness of pear using electronic nose technique

Preparation of Methylcellulose Film-Based CO2 Indicator for Monitoring the Ripeness Quality of Mango Fruit cv. Nam Dok Mai Si Thong

Day-to-day advancements in food science and technology have increased. Indicators, especially biopolymer-incorporated organic dye indicators, are useful for monitoring the ripeness quality of agricultural fruit products. In this investigation, methylcellulose films—containing pH dye-based indicators that change color depending on the carbon dioxide (CO₂) levels—were prepared. The level of CO₂ on the inside of the packaging container indicated the ripeness of the fruit. Changes in the CO₂ level, caused by the ripeness metabolite during storage, altered the pH. The methylcellulose-based film contained pH-sensitive dyes (bromothymol blue and methyl red), which responded (through visible color change) to CO₂ levels produced by ripeness metabolites formed during respiration. The indicator solution and indicator label were monitored for their response to CO₂. In addition, a kinetic approach was used to correlate the response of the indicator label to the changes in mango ripeness. Color changes (the total color difference of a mixed pH dye-based indicator), correlated well with the CO₂ levels in mango fruit. In the ‘Nam Dok Mai Si Thong’ mango fruit model, the indicator response correlated with respiration patterns in real-time monitoring of ripeness at various constant temperatures. Based on the storage test, the indicator labels exhibited color changes from blue, through light bright green, to yellow, when exposed to CO₂ during storage time, confirming the minimal, half-ripe, and fully-ripe levels of mango fruit, respectively. The firmness and titratable acidity (TA) of the fruit decreased from 44.54 to 2.01 N, and 2.84 to 0.21%, respectively, whereas the soluble solid contents (SSC) increased from 10.70 to 18.26% when the fruit ripened. Overall, we believe that the application of prepared methylcellulose-based CO₂ indicator film can be helpful in monitoring the ripeness stage, or quality of, mango and other fruits, with the naked eye, in the food packaging system.

Challenges in assessing the quality of fruit juices: Intervening role of biosensors

The quality of packaged fruit juices is affected during their processing, packaging and storage that might cause deteriorative biological, chemical and physical alterations. Consumption of spoiled juices, either from biological or non-biological sources can pose a potential health hazard for the consumers. Sensitive and reliable methods are required to ensure the quality of fruit juices. Standard analytical methods such as chromatography, spectrophotometry, electrophoresis and titration, that require sophisticated equipment and expertise, are traditionally used to assess the quality of fruit juices. Using biosensors, that are simple, portable and rapid presents a promising alternative to the tedious analytical methods for the detection of various degradation and spoilage indicators formed in the packaged fruit juices. Here, we review the challenges in maintaining the quality of fruit juices and the recent developments in techniques and biosensors for quick analysis of fruit juice components.

Soluble Solids Content Binary Classification of Miyagawa Satsuma in Chongming Island Based on Near Infrared Spectroscopy

Citrus is one of the most important fruits in China. Miyagawa Satsuma, one kind of citrus, is a nutritious agricultural product with regional characteristics of Chongming Island. Near-infrared Spectroscopy (NIR) is a proper method for studying the quality of fruits, because it is low-cost, efficient, non-destructive, and repeatable. Therefore, the NIR technique is used to detect citrus's soluble solid content (SSC) in this study. After obtaining the original spectral data, the first 70% of them are divided into the training set and 30% into the test set. Then, the Random Frog algorithm is chosen to select characteristic wavelengths, which reduces the dimension of the data and the complexity of the model, and accordingly makes the generalization of the classification model better. After comparing the performance of various classifiers (AdaBoost, KNN, LS-SVM, and Bayes) under different characteristic wavelength numbers, the AdaBoost classifier outperforms using 275 characteristic wavelengths for modeling eventually. The accuracy, precision, recall, and F ₁-score are 78.3%, 80.5%, 78.3%, and 0.780, respectively and the ROC (Receiver Operating Characteristic Curve, ROC curve) is close to the upper left corner, suggesting that the classification model is acceptable. The results demonstrate that it is feasible to use the NIR technique to estimate whether the citrus is sweet or not. Furthermore, it is beneficial for us to apply the obtained models for identifying the quality of citrus correctly. For fruit traders, the model helps them to determine the growth cycle of citrus more scientifically, improve the level of citrus cultivation and management and the final fruit quality, and thus increase the economic income of fruit traders.

Application of Nanomaterials to Ensure Quality and Nutritional Safety of Food

Nanomaterials (NMs) are emerging novel tools for preserving quality, enhancing shelf life, and ensuring food safety. Owing to the distinctive physicochemical characters, engineered NMs under varying sizes and dimensions have great potentials for application in the manufacturing, packaging, processing, and safety of quality agrifood. The promise of various kinds of novel NMs that are useful for food industries has opened a possibility of a new revolution in agroprocessing industries in both the emerging and advanced nations. The rapid advancement of nanoscience has provided a great impact on material science that has allowed researchers to understand every aspect of molecular complexity and its functions in life sciences. The reduced size of NMs that increase the surface area is useful in the specific target of different organs, and biodegradable nanospheres are helpful in the transport of bioactive molecules across the cellular barriers. However, nanotechnology creates a great revolution in several sections including agriculture and food industry and also reduces environmental pollution, while the toxicity of some NMs in the food industry poses a great concern to researchers for their greater application. However, most of the developed countries have regulatory control acts but developing countries do not have them yet. Therefore, for the safe use of NMs and also to minimize the health and environmental risks in both the developed and developing countries, it is indispensable to recognize the toxicity-constructed, toxicodynamic, and toxicokinetic features of NMs, which should carefully be emphasized at the home and industrial levels. The current study highlights the updates of the NMs to safeguard the quality and nutritional safety of foods at home and also at the industrial level.

Application of E-nose technique to predict sugarcane syrup quality based on purity and refined sugar percentage

Rapid test methods with portable devices along with standard chemical tests are necessary to determine raw syrup quality in the sugarcane agro-industries. On this account, a special e-nose device was developed to test the sugarcane syrup and its association with the odor emitted from it to determine the amount of sucrose (purity) in the sugarcane syrup. Samples were obtained from the farms of Hakim-Farabi agro-industry, including four varieties (CP57, CP69, IRC99-02, and CP48). Experiments included chemical tests to determine the percentage of purity (PTY) and refined sugar (RS) plus an electronic nose test. Partial least squares (PLS), principle component regression (PCR), multiple linear regression (MLR), and artificial neural network (ANN) methods were used to evaluate the correlation between the gained signals from the sensor array and chemical analysis results of the samples. In the case of PTY, among 8 sensors, MQ3, MQ5, and MQ9 had the highest response compared to the others, while regarding RS, all the sensors except for MQ8 indicated a great contribution. Also, all models for PTY and RS showed a good prediction performance. The results revealed that ANN model, with topology 8-1-2, outperformed others for prediction of the quality indices of sugarcane, with high correlation coefficients (R² = 0.96 for RS; 0.99 for PTY), and relatively low RMSE values of 0.33 for RS; 0.4 for RTY. Finally, findings indicated that e-nose technique has the potential to become an authentic tool to assess chemical features of sugarcane syrup from e-nose system signals.

A nondestructive testing method for soluble solid content in Korla fragrant pears based on electrical properties and artificial neural network

The detection of soluble solid content in Korla fragrant pear is a destructive and time-consuming endeavor. In effort to remedy this, a nondestructive testing method based on electrical properties and artificial neural network was established in this study. Specifically, variations of electrical properties (e.g., equivalent parallel capacitance, quality factor, loss factor, equivalent parallel resistance, complex impedance, and equivalent parallel inductance) of Korla fragrant pears with accumulated temperature were tested using a workbench developed by ourselves. After that the characteristic variables of electrical properties were constructed by principal component analysis (PCA). In addition, three models were constructed to predict SSC in Korla fragrant pears based on the characteristic variables: general regression neural network (GRNN), back-propagation neural network (BPNN), and adaptive network fuzzy inference system (ANFIS). The results indicated that the GRNN model has the best prediction effects of SSC (R 2 = 0.9743, RMSE = 0.2584), superior to that of the BPNN and ANFIS models. Results facilitate a successful, alternative application for rapid assessment of SSC of the maturation stage Korla fragrant pear.

Nanotechnologies in Food Science: Applications, Recent Trends, and Future Perspectives

Nanotechnology is a key advanced technology enabling contribution, development, and sustainable impact on food, medicine, and agriculture sectors. Nanomaterials have potential to lead qualitative and quantitative production of healthier, safer, and high-quality functional foods which are perishable or semi-perishable in nature. Nanotechnologies are superior than conventional food processing technologies with increased shelf life of food products, preventing contamination, and production of enhanced food quality. This comprehensive review on nanotechnologies for functional food development describes the current trends and future perspectives of advanced nanomaterials in food sector considering processing, packaging, security, and storage. Applications of nanotechnologies enhance the food bioavailability, taste, texture, and consistency, achieved through modification of particle size, possible cluster formation, and surface charge of food nanomaterials. In addition, the nanodelivery-mediated nutraceuticals, synergistic action of nanomaterials in food protection, and the application of nanosensors in smart food packaging for monitoring the quality of the stored foods and the common methods employed for assessing the impact of nanomaterials in biological systems are also discussed.

Determination of Odour Interactions in Gaseous Mixtures Using Electronic Nose Methods with Artificial Neural Networks

This paper presents application of an electronic nose prototype comprised of eight sensors, five TGS-type sensors, two electrochemical sensors and one PID-type sensor, to identify odour interaction phenomenon in two-, three-, four- and five-component odorous mixtures. Typical chemical compounds, such as toluene, acetone, triethylamine, α-pinene and n-butanol, present near municipal landfills and sewage treatment plants were subjected to investigation. Evaluation of predicted odour intensity and hedonic tone was performed with selected artificial neural network structures with the activation functions tanh and Leaky rectified linear units (Leaky ReLUs) with the parameter a=0.03. Correctness of identification of odour interactions in the odorous mixtures was determined based on the results obtained with the electronic nose instrument and non-linear data analysis. This value (average) was at the level of 88% in the case of odour intensity, whereas the average was at the level of 74% in the case of hedonic tone. In both cases, correctness of identification depended on the number of components present in the odorous mixture.

Electronic-nose applications for fruit identification, ripeness and quality grading

Fruits produce a wide range of volatile organic compounds that impart their characteristically distinct aromas and contribute to unique flavor characteristics. Fruit aroma and flavor characteristics are of key importance in determining consumer acceptance in commercial fruit markets based on individual preference. Fruit producers, suppliers and retailers traditionally utilize and rely on human testers or panels to evaluate fruit quality and aroma characters for assessing fruit salability in fresh markets. We explore the current and potential utilization of electronic-nose devices (with specialized sensor arrays), instruments that are very effective in discriminating complex mixtures of fruit volatiles, as new effective tools for more efficient fruit aroma analyses to replace conventional expensive methods used in fruit aroma assessments. We review the chemical nature of fruit volatiles during all stages of the agro-fruit production process, describe some of the more important applications that electronic nose (e-nose) technologies have provided for fruit aroma characterizations, and summarize recent research providing e-nose data on the effectiveness of these specialized gas-sensing instruments for fruit identifications, cultivar discriminations, ripeness assessments and fruit grading for assuring fruit quality in commercial markets.

Food analysis using artificial senses

Nowadays, consumers are paying great attention to the characteristics of food such as smell, taste, and appearance. This motivates scientists to imitate human senses using devices known as electronic senses. These include electronic noses, electronic tongues, and computer vision. Thanks to the utilization of various sensors and methods of signal analysis, artificial senses are widely applied in food analysis for process monitoring and determining the quality and authenticity of foods. This paper summarizes achievements in the field of artificial senses. It includes a brief history of these systems, descriptions of most commonly used sensors (conductometric, potentiometric, amperometic/voltammetric, impedimetric, colorimetric, piezoelectric), data analysis methods (for example, artificial neural network (ANN), principal component analysis (PCA), model CIE L*a*b*), and application of artificial senses to food analysis, in particular quality control, authenticity and falsification assessment, and monitoring of production processes.

Diverse applications of electronic-nose technologies in agriculture and forestry

Electronic-nose (e-nose) instruments, derived from numerous types of aroma-sensor technologies, have been developed for a diversity of applications in the broad fields of agriculture and forestry. Recent advances in e-nose technologies within the plant sciences, including improvements in gas-sensor designs, innovations in data analysis and pattern-recognition algorithms, and progress in material science and systems integration methods, have led to significant benefits to both industries. Electronic noses have been used in a variety of commercial agricultural-related industries, including the agricultural sectors of agronomy, biochemical processing, botany, cell culture, plant cultivar selections, environmental monitoring, horticulture, pesticide detection, plant physiology and pathology. Applications in forestry include uses in chemotaxonomy, log tracking, wood and paper processing, forest management, forest health protection, and waste management. These aroma-detection applications have improved plant-based product attributes, quality, uniformity, and consistency in ways that have increased the efficiency and effectiveness of production and manufacturing processes. This paper provides a comprehensive review and summary of a broad range of electronic-nose technologies and applications, developed specifically for the agriculture and forestry industries over the past thirty years, which have offered solutions that have greatly improved worldwide agricultural and agroforestry production systems.

Tomato Quality during Short-Term Storage Assessed by Colour and Electronic Nose

An assay based on an electronic olfactory system was set to evaluate tomato fruits by sensing the aromatic volatiles during postharvest storage of 21 days at C in darkness. Olfactory system measurements were coupled with colour values. Odour profile and senescence parameters were carried out at 7-day intervals. Discriminant function analysis applied to electronic nose data showed three components, accounting for 99.2% of the total variance. In the present assay, separation among groups according to storage time (0, 7, and 14 days) was observed for wildtype. Overexpressed (Money Maker) lines/plants of tomato showed difference between odour profile for day 0 and day 21, even tough a no clear discrimination between 7 and 14 days was observed. Fruit lost weight almost linearly with shelf life () presenting an averaged loss of 21% () for over-expressed (Money Maker) lines/plants, 13% () for silenced (Money Maker), and 14% () for wild type during 21 days of storage. Colour values , , and data showed that colour properties changed during storage for all the lines considered. Correlations between odour profiles and colour parameter were obtained showing that the electronic nose is a useful technique for monitoring short-term storage of tomato.

Invited review article: an odor-sensing system--powerful technique for foodstuff studies

This work examines gas sensor array technology combined with multivariate data processing methods and demonstrates a promising potential for rapid, non-destructive analysis of food. Main attention is focused on detailed description of sensor used in e-nose instruments, construction, and principle of operation of these systems. Moreover, this paper briefly reviews the progress in the field of artificial olfaction and future trends in electronic nose technology, namely, e-nose based on mass spectrometry. Further discussion concerns a comparison of artificial nose with gas chromatography-olfactometry and the application of e-nose instruments in different areas of food industry.

Processing characteristics and flavour of full lotus root powder beverage

BACKGROUND

Lotus root beverage is commonly made from raw lotus root (RLR). However, RLR production is strictly limited, because it is prone to decomposition and browning after its short harvest season. In this study an innovative beverage was prepared from full lotus root powder (FLRP) as a substitute for RLR in an attempt to solve this problem.

RESULTS

The components of FLRP basically corresponded to those of RLR, but there was some loss of heat-labile compounds. Using differential scanning calorimetry, a gelatinisation temperature range of 57.08-67.80 °C was determined for FLRP with an average particle size distribution of 70 µm. The optimal conditions for enzymatic treatment of FLRP beverage were determined by response surface methodology as an enzyme concentration of 2.2 g kg(-1) at 53 °C for 86 min. Turbidity decreased from 1082 to 280 nephelometric turbidity units following enzymolysis. Properties of FLRP beverage were also studied and a qualitative comparison of flavour compounds between RLR and FLRP beverages was made by electronic nose.

CONCLUSION

Basic flavour compounds were consistent and flavour radar plots had approximately the same shape, area and proportion when all ingredients were identical apart from FLRP and RLR. Therefore, in terms of flavour, FLRP beverage appears to be a feasible substitute for RLR beverage.

Identification of tea storage times by linear discrimination analysis and back-propagation neural network techniques based on the eigenvalues of principal components analysis of e-nose sensor signals

An electronic nose (E-nose) was employed to detect the aroma of green tea after different storage times. Longjing green tea dry leaves, beverages and residues were detected with an E-nose, respectively. In order to decrease the data dimensionality and optimize the feature vector, the E-nose sensor response data were analyzed by principal components analysis (PCA) and the five main principal components values were extracted as the input for the discrimination analysis. The storage time (0, 60, 120, 180 and 240 days) was better discriminated by linear discrimination analysis (LDA) and was predicted by the back-propagation neural network (BPNN) method. The results showed that the discrimination and testing results based on the tea leaves were better than those based on tea beverages and tea residues. The mean errors of the tea leaf data were 9, 2.73, 3.93, 6.33 and 6.8 days, respectively.