An original approach for the quantitative characterization of saffron aroma strength using electronic nose

A non-invasive, sensor-based approach to exploit the autofluorescence of saffron (Crocus sativus L.) for on-site evaluation of aging

So far, compliance with ISO 3632 standard specifications for top-quality saffron guarantees good agricultural and post-harvest production practices. Tracking early-stage oxidation remains challenging. Our study aims to address this issue by exploring the visible, fluorescence, and near-infrared spectra of category I saffron. Using a multi-spectral sensor, we tested fresh and artificially aged saffron in powder form. High autofluorescence intensities at 600-700 nm allowed calibration for the 'content of aged saffron'. Samples with minimum coloring strength (200-220 units) were classified as 70% aged, while those exceeding maximum aroma strength (50 units) as 100% aged. Consistent patterns across origin, age, and processing history indicated potential for objectively assessing early-oxidation markers. Further analyses uncovered multiple contributing fluorophores, including cis-apocarotenoids, correlated with FTIR-based aging markers. Our findings underscore that sensing autofluorescence of traded saffron presents an innovative quality diagnostic approach, paving new research pathways for assessing the remaining shelf-life along its supply chain.

Pomegranate molasses authentication using hyperspectral imaging system coupled with automatic clustering algorithm

Pomegranate molasses is made from concentrated pomegranate juice with nothing added. Due to its nutritional value, limitation in production, and high production cost, this product may be adulterated by date syrup. This study was done to differentiate various types of pomegranate molasses and investigate the possibility of nonauthenticity detection in pomegranate molasses samples using the hyperspectral imaging (HSI) technique compared with physicochemical measurement analysis. The physicochemical properties (brix index, sucrose, acidity, ash content, pH, and formalin index) of 24 samples were measured as the reference analysis method, and it was found that the formalin index was a good factor for pomegranate molasses authenticity evaluation. Additionally, an HSI system (400-1000 nm) was used as a nondestructive and rapid screening method to capture spectral data of the samples. The evolutionary wavelength selection algorithm was applied to select effective wavelengths in sample clustering based on the obtained Davies-Bouldin index. Next, principal component analysis was used to visually interpret the spectral data of the sample when using the selected wavelengths and the whole spectra of the samples. Finally, an automatic clustering algorithm by the artificial bee colony as an unsupervised method was developed for the clustering of the authentic and nonauthentic samples. The method did not need descriptively labeled samples and obtained agreed satisfactorily with the degree of nonauthenticity in the samples. This study showed that the developed HSI technique coupled with an automatic clustering algorithm could detect date syrup nonauthenticity in pomegranate samples from the level of 5% adulteration.

Recent Progress in Smart Electronic Nose Technologies Enabled with Machine Learning Methods

Machine learning methods enable the electronic nose (E-Nose) for precise odor identification with both qualitative and quantitative analysis. Advanced machine learning methods are crucial for the E-Nose to gain high performance and strengthen its capability in many applications, including robotics, food engineering, environment monitoring, and medical diagnosis. Recently, many machine learning techniques have been studied, developed, and integrated into feature extraction, modeling, and gas sensor drift compensation. The purpose of feature extraction is to keep robust pattern information in raw signals while removing redundancy and noise. With the extracted feature, a proper modeling method can effectively use the information for prediction. In addition, drift compensation is adopted to relieve the model accuracy degradation due to the gas sensor drifting. These recent advances have significantly promoted the prediction accuracy and stability of the E-Nose. This review is engaged to provide a summary of recent progress in advanced machine learning methods in E-Nose technologies and give an insight into new research directions in feature extraction, modeling, and sensor drift compensation.

Latest advances in the extraction and determination of saffron apocarotenoids

Saffron, the dried red stigmas of Crocus sativus L. plant, is the most expensive spice in the world. It is highly valued not only for the color and flavor that exerts to various foods and drinks but also for its functional properties. Various classes of apocarotenoids such as crocetin sugar esters, picrocrocin and safranal are responsible for these characteristics. In the present review, the evolution of the methods proposed by the ISO standard for extraction and determination of saffron apocarotenoids since 1980 is presented in parallel to other approaches proposed by various scientists to overcome limitations of the standard. Moreover, the latest advances regarding applications of novel extraction techniques and powerful analytical tools that require limited or no sample preparation are critically discussed.