Sinusoidal envelope voltammetry as a new readout technique for electronic tongues

"Reference sample comparison method": A new voltammetric electronic tongue method and its application in assessing the shelf life of fresh milk

Shelf life is a critical comprehensive indicator of food quality. Voltammetric electronic tongue (V-Et), is well-suited for assessing food shelf life, due to its capable of capturing food overall fingerprints. This study designed a "reference sample comparison method" for V-Et to assess the shelf life of fresh milk. Quality differences between milk samples of different shelf lives and reference samples were quantified by differential degree (Dd) values. A new "one-to-one" model of milk shelf life was established based on Dd values, and significantly improved predictive accuracy by 11.14 %-17.17 % and 14.86 %-44.47 % in overall quality shelf life assessment compared to "many-to-one" models based on SVM and DFA. Even in the more sophisticated evaluation of microbial safety and sensory quality shelf life, it attained relative errors of 13.57 % and 7.68 %, respectively. All these findings showed the significant potential of the "reference sample comparison method" in assessing food shelf life with V-Et.

Development of a Bitterness Sensor Using Partially Dissociated Amine Compounds

This study focused on developing an advanced bitterness sensor designed to minimize interference from common anions such as nitrate (NO3-) and iodide (I-) by incorporating partially dissociated amine compounds into the sensor membrane. The conventional bitter sensor (C00) uses fully dissociated quaternary ammonium salt tetradecyl ammonium bromide (TDAB), which typically exhibits high responses to these anions, leading to inaccurate bitterness assessments. To address this issue, we explored the use of three partially dissociated amines-oleylamine (OAm), dioctadecylamine (DODA), and tridodecylamine (TDA)-as lipids in the membrane components. We fabricated sensor membranes and tested their ion selectivity, interference resistance to anion, and sensitivity to iso-alpha acids (IAAs), representative bitter compounds in beer. The results showed that the membranes with partially dissociated amines significantly reduced anion interference. Notably, the sensitivity of the TDA membrane to IAAs was 80.4 mV/dec in concentration, exceeding the 68.5 mV/dec of the TDAB membrane. This enhanced sensitivity, coupled with reduced anion interference, reveals a novel property of partially dissociated lipids in taste sensors, distinguishing them from fully dissociated lipids. These findings pave the way for the development of sensors that can accurately assess a bitter taste and have potential applications in the food and beverage industry.

Effects of Modified Atmosphere Packaging with Varied CO2 and O2 Concentrations on the Texture, Protein, and Odor Characteristics of Salmon during Cold Storage

The effect of gas ratio on the growth of bacteria has been well demonstrated, but some adverse effects of modified atmosphere packaging (MAP) on seafoods have also been found. To provide a better understanding of the effects of CO₂ and O₂ concentrations (CO₂ from 40% to 100% and O₂ from 0% to 30%) in MAP on the texture and protein contents and odor characteristics of salmon during cold storage, the physiochemical, microbial, and odor indicators were compared with those without treatment (CK). Generally, MAP treatments hindered the increase of microbial counts, total volatile basic nitrogen, and TCA-soluble peptides, and decreased the water-holding capacity, hardness, springiness, and sarcoplasmic and myofibrillar protein contents. The results also indicated that 60%CO₂/10%O₂/30%N₂ was optimal and decreased the total mesophilic bacterial counts by 2.8 log cfu/g in comparison with CK on day 12. In agreement, the concentration of CO₂ of 60% showed the lowest myofibrillar protein degradation, and less subsequent loss of hardness. The electronic nose characteristics analysis indicated that 60%CO₂/20%O₂/20%N₂ and 60%CO₂/10%O₂/30%N₂ had the best effect to maintain the original odor profiles of salmon. The correlation analysis demonstrated that microbial growth had a strong relationship with myofibrillar and sarcoplasmic protein content. It can be concluded that 60%CO₂/10%O₂/30%N₂ displayed the best effect to achieve the goal of preventing protein degradation and odor changes in salmon fillets.

Geographical origin identification of two salmonid species via flavor compound analysis using headspace-gas chromatography-ion mobility spectrometry combined with electronic nose and tongue

The flavor of salmonids is affected by species and origin. Sources of salmonid fish fillets are complex and difficult to identify and label fraud occasionally occurs in the market. In this study, headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS), electronic nose, electronic tongue and amino acid detection technologies were used to analyze flavor compounds in two salmonid species from different geographical origins. Fingerprints of volatile compounds of salmonid were constructed using HS-GC-IMS technology. Free amino acid (FAA) content differed in salmonids from different geographical origins. Regarding salmonid odor, HS-GC-IMS analysis results were basically consistent with those of the electronic nose. Regarding taste, the conclusions drawn from the electronic tongue were consistent with the amino acid test results. Therefore, our results demonstrate that flavor compounds can be used to distinguish salmonids from different geographical origins, providing a new dimension to food safety and authenticity. Furthermore, HS-GC-IMS, electronic nose and tongue can be used as tools in the market to identify food fraud.

Electronic Tongue Recognition with Feature Specificity Enhancement

As a kind of intelligent instrument, an electronic tongue (E-tongue) realizes liquid analysis with an electrode-sensor array and certain machine learning methods. The large amplitude pulse voltammetry (LAPV) is a regular E-tongue type that prefers to collect a large amount of response data at a high sampling frequency within a short time. Therefore, a fast and effective feature extraction method is necessary for machine learning methods. Considering the fact that massive common-mode components (high correlated signals) in the sensor-array responses would depress the recognition performance of the machine learning models, we have proposed an alternative feature extraction method named feature specificity enhancement (FSE) for feature specificity enhancement and feature dimension reduction. The proposed FSE method highlights the specificity signals by eliminating the common mode signals on paired sensor responses. Meanwhile, the radial basis function is utilized to project the original features into a nonlinear space. Furthermore, we selected the kernel extreme learning machine (KELM) as the recognition part owing to its fast speed and excellent flexibility. Two datasets from LAPV E-tongues have been adopted for the evaluation of the machine-learning models. One is collected by a designed E-tongue for beverage identification and the other one is a public benchmark. For performance comparison, we introduced several machine-learning models consisting of different combinations of feature extraction and recognition methods. The experimental results show that the proposed FSE coupled with KELM demonstrates obvious superiority to other models in accuracy, time consumption and memory cost. Additionally, low parameter sensitivity of the proposed model has been demonstrated as well.