# Hyperspectral Imaging as a Rapid Quality Control Method for Herbal Tea Blends
## 1 Introduction
Accidental contamination or deliberately substituting raw materials with a relative species or one of reduced economic value or undeclared fillers is a potential quality concern [12]. Thus, herbal tea authenticity is an issue of food quality and safety. An important aspect in quality control (QC) is to ensure that a product contains exactly what is claimed on the packaging or package insert, with regards to constituents and quantities. However, several reports have shown that most herbal products do not always meet this quality requirement as adulterants and sometimes incorrect botanical species not indicated on the label have been detected resulting in some products being withdrawn from the market. These quality control issues can be overcome by instituting cost-effective, routine quality control procedures during the manufacturing processes [13]. Chemical fingerprinting based on various analytical techniques has been used for the identification, authentication and evaluation of the quality of herbal medicines and related products. Some chromatographic techniques used for chemical fingerprinting include high-performance thin layer chromatography (HPTLC), high-performance liquid chromatography (HPLC), gas chromatography coupled to mass spectrometry (GC-MS) and liquid chromatography coupled to mass spectrometry (LC-MS) [10,14,15,16]. The advantages of these methods include good repeatability, wide applicability, and reasonable qualitative and quantitative capabilities [14]. These methods are however costly, require cumbersome sample preparation, skilled personnel and longer total analysis time. Spectroscopy, on the other hand is a fast, accurate, more cost-efficient, non-destructive method that does not require sample preparation and it can be regarded as an alternative to traditional chemical analysis methods. Near-infrared (NIR) spectroscopic analysis has been implemented with success in the agricultural, petrochemical, textile and pharmaceutical industries [17]. NIR spectroscopy has been successfully applied to determine the quality of black tea through the prediction of moisture and theaflavin content [18]. Several methods to determine the quality of green tea usingNIR spectroscopy and chemometric methods have been developed. Calibration models constructed could be used to predict the content of caffeine, epigallocatechin gallate (EGCG), epicatechin (EC), epigallogatechin (EGC) and epicatechin gallate (ECG) as well as total antioxidant capacity [17; 19; 20]. Diffusion reflectance Fourier transform mid-infrared spectroscopy in combination with chemometric discrimination methods has been applied with success to distinguish between twelve different tea varieties including several black, green and semifermented oolong teas [21]. Hyperspectral imaging (HSI) is a technique that combines spectroscopy and digital imaging technologies to capture the composition (spectral data) as well as spatial distribution of constituents within a sample of interest [13; 22]. In the past decade, the technique has found applications for quality assessment of raw materials and products in various sectors such as agricultural, food and pharmaceutical industries [23; 24]. Hyperspectral imaging is used to acquire a spectrum for each pixel in the imaging scene with the purpose of finding objects or identifying materials. The acquired image can then be analyzed using multivariate algorithms for visualization of chemical constituents and their distribution within a matrix [17]. In this study, HSI was investigated as a fast, visual and non-destructive technique in the differentiation of herbal tea raw materials. Furthermore, the technique was used to quantitatively determine the composition of herbal tea blends (bags). The following blends were investigated; (1) rooibos (_Aspalathus linearis_ (Burm.f.) [PERSON]) and buchu (_Agathosma betulina_ ([PERSON]) [PERSON]); (2) green rooibos (_A. linearis_) and green honeybush (_Cyclopia intermedia_ [PERSON]); and (3) rooibos (_A. linearis_) and cancer bush (_Sutherlandania frutescens_ (L.) [PERSON]).