Innovative Hyperspectral Imaging Advances Black Garlic Quality Control

In a recent study published in Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, Xingyi Huang and colleagues from Jiangsu University proposed a novel method for improving and monitoring the quality control of black garlic (1). Their method, which employed hyperspectral imaging (HSI) technology combined with chemometric algorithms, was shown to be effective.

Black garlic is a popular commodity in the global economy. It is used in many culinary dishes. Black garlic is produced by subjecting raw garlic to a controlled heating process, which enhances its antioxidant properties and transforms its chemical composition, aroma, and flavor (1). Black garlic is known to have several health benefits. As a natural anti-inflammatory, it can help with overall brain health (2). Black garlic also contains more antioxidants as a result of its controlled heating process and helps regulate blood sugar (2). Because of these health benefits, it is a product in high demand; however, ensuring consistent quality across processing stages has remained a significant challenge for producers. The Jiangsu University team developed an efficient approach that not only identifies distinct processing stages with great accuracy, but it also predicts key physicochemical properties critical to product quality (1).

Gourmet black garlic. | Image Credit: © Eskymaks - stock.adobe.com.

A main component to this study was the research team’s use of a visible-near-infrared (vis-NIR) HSI system. The research team used this system because this imaging technology had the ability to capture spectral information across a wide range of wavelengths (1). As a result, the Vis-NIR HSI system enabled the research team to conduct a comprehensive analysis of the garlic samples at different stages of processing (1). The hyperspectral data was processed using advanced noise reduction techniques, including Savitzky-Golay (SG), standard normal variate (SNV), and multiplicative scatter correction (MSC), to ensure the accuracy of the models (1).

To reduce computational complexity while retaining critical spectral information, the researchers also applied feature screening methods, such as competitive adaptive reweighted sampling (CARS) and variable combination population analysis (VCPA) (1). CARS selects important spectral variables (signals at specific wavelengths) by simulating an adaptive process, using reweighting, and eliminates less relevant variables. VCPA identifies the best variable subsets by testing combinations within a population, often using genetic algorithms (GA) for optimization.

The quality of black garlic requires differentiating between process stages. To do this, the researchers employed several pattern recognition methods to classify the garlic samples (1). These pattern recognition methods included linear discriminant analysis (LDA), k-nearest neighbor (KNN), and support vector machine classification (SVC) (1). Among the pattern recognition methods used, SVC performed the best, delivering a discriminant accuracy of 98.46% (1).

In addition to stage discrimination, the team developed robust models for predicting essential quality indicators, specifically moisture content and 5-hydroxymethylfurfural (5-HMF), which is the main compound linked to the garlic's antioxidant properties (1). To analyze the spectral data, the research team used partial least squares regression (PLSR) and support vector machine regression (SVR) (1).

Between the SVR and PLSR models, it was determined that the PLSR model was the better method. The PLSR model obtained correlation coefficients of prediction of 0.9762 for moisture and 0.9744 for 5-HMF (1). These high correlation values underscore the reliability of the proposed method for quantitative analysis during black garlic processing.

By enabling rapid, accurate, and non-destructive quality assessments, the vis-NIR HSI system integrated with chemometric algorithms can streamline production processes and reduce waste. The research not only benefits black garlic production, but it also highlights the broader applicability of HSI in food science (1). Through the integration of vis-NIR HSI systems with advanced chemometric techniques, the research team highlighted a new method that could be applied to various food items, including more agricultural products (1).

As the demand for functional foods like black garlic continues to rise, technologies such as those demonstrated by the research team will play an increasingly vital role in meeting consumer expectations.

References

1. Yu, S.; Huang, X.; Xu, F.; et al. Production Monitoring and Quality Characterization of Black Garlic Using Vis-NIR Hyperspectral Imaging Integrated with Chemometrics Strategies. Spectrochimica Acta Part A: Mol. Biomol. Spectrosc. 2025, 326, 125182. DOI: 10.1016/j.saa.2024.125182
2. Cleveland Clinic, 4 Health Benefits of Black Garlic. Cleveland Clinic. Available at: https://health.clevelandclinic.org/benefits-of-black-garlic (accessed 2025-01-08).