Assessment of cooked alpaca and llama meats from the statistical analysis of data collected using an ‘electronic nose’

For reasons related to poverty or lack of quality control over food in some countries of the world, there is still food adulteration. Low-cost meats such as donkey or pork are marketed as lamb or beef. This is morally dangerous but may be more dangerous for some people who are allergic to certain types of meat or who have religious reservations. With the rapid development of artificial intelligence techniques, it is possible to build a model capable of differentiating between different types of meat. This study aims to build a model capable of differentiating between different types of red meat. It also aims to compare performance between the very state of art CNN in computer vision with the transformer architecture. For this goal, a limited dataset from an online repository was obtained. The dataset contains RGB images of beef, horse, and pork meats. The images were processed, and various data augmentation techniques were applied. Then vision transformer ViT and mobile net models with and without fine-tuning were built. To measure the models' behavior, several performance evaluation criteria were applied. The best testing accuracy is 97% achieved by the fine-tuned ViT model. This study showed the effectiveness of applying the transformer architecture and especially the fine-tuned ViT model in the areas of image classification even on a limited dataset.

Silver carp is a one of the most important freshwater fish species in China, and is popular when making soup in the Chinese dietary culture. In order to investigate the profile of fish soup tastes and flavours cooked using different regions of the same fish, the silver carp was cut into four different regions: head, back, abdomen, and tail. The differences in taste and flavour of the four kinds of homemade fish soup were investigated by an electronic nose and electronic tongue. The basic chemical components of the different fish regions and the SDS-PAGE profile of the fish soup samples were investigated. Two chemometrics methods (principal component analysis and discriminant factor analysis) were used to classify the odour and taste of the fish soup samples. The results showed that the electronic tongue and nose performed outstandingly in discriminating the four fish soups even though the samples were made from different regions of the same fish. The taste and flavour information of different regions of the silver carp fish could provide the theoretical basis for food intensive processing.<br /><br />

Artificial olfaction, also known as an electronic nose, is a gas identification device that replicates the human olfactory organ. This system integrates sensor arrays to detect gases, data acquisition for signal processing, and data analysis for precise identification, enabling it to assess gases both qualitatively and quantitatively in complex settings. This article provides a brief overview of the research progress in electronic nose technology, which is divided into three main elements, focusing on gas-sensitive materials, electronic nose applications, and data analysis methods. Furthermore, the review explores both traditional MOS materials and the newer porous materials like MOFs for gas sensors, summarizing the applications of electronic noses across diverse fields including disease diagnosis, environmental monitoring, food safety, and agricultural production. Additionally, it covers electronic nose pattern recognition and signal drift suppression algorithms. Ultimately, the summary identifies challenges faced by current systems and offers innovative solutions for future advancements. Overall, this endeavor forges a solid foundation and establishes a conceptual framework for ongoing research in the field.

Since gas sensor drift is a main limitation for the application of an electronic nose, and a reference standard is necessary for shelf management of litchi fruit, a modified mean deviation threshold function based on fast Fourier transform (MDFF–FFT) for electronic nose drift elimination and a new concept the rest storage life (RSL) for litchi fruit shelf situation evaluation have been constructed in this study. Three commonly used threshold acquisition methods, unbiased estimator, fixed threshold, and mini-max principle were evaluated to instead of selecting threshold value randomly for present MDFF–FFT. A PEN3 portable electronic nose was applied to recognize the RSL of litchi during storage across room temperature (RT), refrigerator environment (RE) and controlled-atmosphere (CA) environments. Linear discriminant analysis (LDA), probabilistic neural network (PNN), and partial least squares regression (PLSR) were used to compare the RSL classification effect, recognition accuracy, and predict ability of litchi stored in the three environments based on electronic nose with the drift elimination of different threshold acquisition methods using MDTF–FFT. The results showed that an electronic nose has the potential to recognize the RSL of litchi stored in different environments. Unbiased estimator method can provide better threshold than other threshold acquisition methods for MDTF–FFT. After drift elimination by unbiased estimator method combined with MDTF–FFT, litchi RSL can be classified, recognized and predicted by electronic nose effectively, the accuracy of which was higher than control (no drift elimination) and drift elimination with other methods.

Intelligent detection of flavor changes in ginger during microwave vacuum drying based on LF-NMR

Peony seed oil has recently been introduced as a high-quality food oil. Because the high price of peony seed oil may tempt unscrupulous merchants to dilute it with cheaper substitutes, a rapid detection method for likely adulterants is required. In this study, the fatty acid composition of peony seed oil and four less expensive edible oils (soybean oil, corn oil, sunflower oil, and rapeseed oil) were measured by gas chromatography mass spectrometry. Peony oil adulterated by other edible oils was assessed using iodine values to estimate the extent of adulteration. Adulteration was also measured using an electronic nose (E-nose) combined with principal component analysis (PCA) or linear discriminant analysis (LDA). Results indicated that peony seed oil was highly enriched in α-linolenic acid. Although the iodine value can be used to detect some adulterants by measuring unsaturation, it was not able to detect all four potential adulterants. In contrast, the E-nose can rapidly identify adulterated peony seed oil by sampling vapor. Data analyses using PCA and LDA show that LDA more effectively clusters the data, discriminates between pure and adulterated oil, and can detect adulteration at the 10% level. E-nose combined with LDA suitable for detection of peony seed oil adulteration.

The aim of the present study was to develop an electronic nose for the quality control of red meat. Electronic nose and bacteriological measurements are performed to analyse samples of beef and sheep meat stored at 4°C for up to 15 days. Principal component analysis (PCA) and support vector machine (SVM) based classification techniques are used to investigate the performance of the electronic nose system in the spoilage classification of red meats. The bacteriological method was selected as the reference method to consistently train the electronic nose system. The SVM models built classified meat samples based on the total microbial population into "unspoiled" (microbial counts < 6 log10 cfu/g) and "spoiled" (microbial counts ≥ 6 log10 cfu/g). The preliminary results obtained by the bacteria total viable counts (TVC) show that the shelf-life of beef and sheep meats stored at 4 °C are 7 and 5 days, respectively. The electronic nose system coupled to SVM could discriminate between unspoiled/ spoiled beef or sheep meats with a success rate of 98.81 or 96.43 %, respectively. To investigate whether the results of the electronic nose correlated well with the results of the bacteriological analysis, partial least squares (PLS) calibration models were built and validated. Good correlation coefficients between the electronic nose signals and bacteriological data were obtained, a clear indication that the electronic nose system can become a simple and rapid technique for the quality control of red meats.

Golden pompano (Trachinotus ovatus) quality forecasting method utilising Vis/NIR spectroscopy combined with electronic nose (EN) was investigated in this article. Responses of Vis/NIR spectroscopy and EN to pompanos stored at 4°C were measured for 6 days. Physical/chemical indexes including texture, total volatile basic nitrogen, pH, total viable counts, and human sensory evaluation were synchronously examined as quality references. Chemometric methods including principal component analysis (PCA) and stochastic resonance (SR) were employed for spectroscopic and EN data analysis. Physicochemical examination demonstrated that fish quality decreased rapidly during storage. PCA qualitatively classified freshness degree of pompano samples, while SR signal-to-noise ratio (SNR) spectrum using SNR maximum quantitatively characterised quality for all samples. Golden pompano quality predictive models were developed based on spectroscopy, EN, and spectroscopy combined with EN, respectively. Results demonstrated that the model developed based on spectroscopy combined with EN presented a forecasting accuracy of 93.3%.

Ventilator-associated pneumonia (VAP) is a frequent complication in patients in surgical intensive care units. Pneumonia scores, chest radiography, and bronchoscopy are all employed, but there is no gold standard test for the diagnosis of VAP. The electronic nose, a sensor of volatile molecules, is well suited to testing the breath of mechanically ventilated patients. Our objective was to determine the potential use of an electronic nose as a diagnostic adjunct in the detection of VAP. We performed a prospective study of mechanically ventilated patients in a surgical intensive care unit. Clinical data, including temperature, white blood cell count, character and quantity of tracheal secretions, ratio of partial pressure of arterial oxygen to fraction of inspired oxygen, and chest radiographs, were collected, and a pneumonia score between 0 and 10 was calculated. Exhaled gas was sampled from the expiratory limb of the ventilator circuit. The gases were assayed with a commercially available electronic nose. Multidimensional data reduction analysis was used to analyze the results. Forty-four patients were studied. Fifteen patients had pneumonia scores of 7 or greater, and 29 patients had scores of 6 or less. With Fisher discriminant analysis and K-nearest neighbor analysis, the electronic nose was able to discriminate between the two groups. The electronic nose is a new technology that is inexpensive, noninvasive, and portable. We demonstrate its ability to predict pneumonia, based on a well-recognized scoring system. This technology promises to serve as a diagnostic adjunct in the management of VAP.

Heterocyclic aromatic amines (HAAs), the potential mutagens/carcinogens, are formed during cooking meat at higher temperature. The current study was designed to evaluate the effects of various cooking methods, i.e., pan frying, deep frying, charcoal grilling and roasting, on the formation of HAAs in three different types of meat, i.e., beef, chicken and mutton. Physicochemical analysis of thermally treated meat was carried out. Mineral profile such as Na, K, Fe, Cu, Zn and Mg was also determined. Two mutagenic HAAs, i.e., 1-methyl-9H-pyrido [4,3-b]indole (Harman) and 9H-pyrido-[4,3-b]indole (Norharman), were formed during cooking and extracted by solid phase extraction technique and analyzed by high-performance liquid chromatography using internal standards. The results showed that the amounts of Harman were higher than Norharman. Generally, the type and content of HAAs in cooked meat varies with cooking methods and conditions. Practical Applications Diet is one of the major factors that influence the tumor formation in humans. Epidemiologic and experimental evidence indicates that proteinaceous foods, especially meat cooked at higher temperatures, may lead to the formation of heterocyclic aromatic amines (HAAs). HAAs are the potential mutagens and contribute toward the carcinogenicity depending on many factors, including type of meat and cooking method. They vary in their chemical nature, mechanism of formation, amounts in different types of meat and meat products. They also show a variable range of metabolic behavior and carcinogenic potential. The exact identification of major risk factors, especially dietary carcinogens, could play a vital role in reducing cancer incidence and mortality. The present study was designed to investigate the influence of different cooking methods (pan frying, deep frying, charcoal grilling and roasting) on the formation of HAAs in various meats (chicken, beef and mutton). It is also evident from the results that the content of HAAs in cooked meat varied with cooking methods and cooking conditions, and data from this study could be used to assess the human intake of HAAs and also to identify conditions that minimize the formation and intake of HAAs.

This study explored the feasibility of achieving rapid nondestructive detection of browning in potatoes from a visual and olfactory perspective. In five grades, 15 pieces were taken out from each batch, respectively, and tested for browning characteristics using Chroma meter (CM), machine vision (MV), and electronic nose (E‐nose). Linear discriminant analysis, support vector machine (SVM), K‐Nearest Neighbor, and Back Propagation Artificial Neuron Network were used to classify the samples. The discriminant accuracy of the MV and E‐nose datasets was 0.960 and 0.813, respectively, and this increased to 1,000 after data fusion. Partial least squares regression and SVM regression were also used to investigate the correlation between two data sets and CM data. The MV results for L*, a*, and b* were 0.864, 0.966, and 0.992, with variances of 1.062, 0.575, and 0.123, respectively, indicating a very strong correlation. The predicted result of E‐nose for a* reached 0.705, and the variance was 1.815, which also showed a high correlation. The data fusion models for L*, a*, and b* yielded 0.883, 0.968, and 0.997, with variances of 1.264, 0.633, and 0.083, respectively. These results indicated that MV and E‐nose can be used as nondestructive detection tools for detecting browning in potatoes.Practical ApplicationsPotato products are popular among consumers because of its rich nutrition and processing characteristics. However, enzymatic reactions during peeling and processing of fresh‐cut potato results in color defects which compromise the quality of the final products. Therefore, it is an inevitable requirement for the development of the industry to adopt appropriate methods to detect and prevent browning in fresh‐cut potatoes as a means of strengthening quality management. The present study explored the feasibility of rapid nondestructive detection of browning in potato from the visual and olfactory aspects. A new idea for the application of machine vision and electronic nose in the field of nondestructive detection of agricultural products was proposed. At the same time, this article has certain reference value to the quality management of potato and other fruit and vegetable processing industry.

The flavor of onions at different processing times (0, 6, 14 20 days) were monitored by electronic nose. Different sensors were sensitive to different compounds. The results showed that the sulfur compounds were the major components in the onions, which decreased significantly with the increasing of the processing time. What is more, the flavor of the onions could be differentiated clearly by electronic nose. The linear discriminant analysis (LDA) model could be applied to distinguish the samples efficiently.

The classification of the aroma pattern of pork oil and canola oil on the chemometric-based electronic nose has been classified. The electronic nose used is a series of chemical sensors combined in parallel. Sensors are made of semiconductor material that can detect changes in gas in the air. Each sample measured by an electronic nose provides output in the form of different voltages on each sensor. The data processing method used is Linear Discriminant Analysis (LDA) which is able to classify based on patterns. The samples used were canola oil, pork oil and a mixture of pork oil and canola oil with a percentage of 50%: 50%. The results of the classification of electronic nose patterns with samples of pork oil and canola oil show that each sample is fairly well clustered with the value of the first disk function is 99.9% and the second discriminant function value is 0.1%.

Evaluation of different storage conditions of extra virgin olive oils with an innovative recognition tool built by means of electronic nose and electronic tongue

An electronic nose (E-nose) was developed to distinguish different stored eggs by using principal component analysis (PCA),linear discriminant analysis (LDA),BP neural network (BPNN) and the combination of a genetic algorithm and BP neural network (GANN). Results showed that the E-nose can distinguish eggs of different storage time under cool and room-temperature storage by LDA and PCA. LDA preceded PCA. Better prediction values were obtained by GANN than by BPNN. Relationships were established between the E-nose signal and egg quality indices by quadratic polynomial step regression (QPSR). The prediction models indicated good prediction performance with correlation coefficient between predicted and measured values greater than 0.90.