Hepatitis C virus (HCV) is a hepatotropic RNA virus that causes progressive liver damage [1]. It causes acute hepatitis which develops into a chronic infection in 50–80% of infected patients. This leads to the triggering of a chronic inflammation process that may lead to liver fibrosis, cirrhosis, hepatocellular carcinoma, and death [1,2]. Fibrogenesis is the chief complication associated with chronic HCV infection which leads to progressive liver fibrosis and ultimately cirrhosis [3,4].

Major risk factors associated with the spread of infection are unsterile medical procedures (iatrogenic infections) and shared use of needles by the drug addicts [5]. The diagnosis of infection is based on serum HCV antibody testing and HCV RNA genome detection. For treatment, after an era that was dominated by the interferon-based therapies, various direct-acting antiviral agents (DAAs) are now available. When used in combination, these DAAs can cure (defined as a sustained virological response 12 weeks after treatment) >90% of infected patients. Evaluation of cirrhotic and non-cirrhotic status of patient is evaluated on the basis of serum markers (APRI-FIB4) and imaging techniques as per World Health Organization (WHO) guidelines [6]. This assessment of non-cirrhotic/cirrhotic status is important for determining the treatment regime by the practitioner, who will decide the duration and combination of the DAA for achieving sustained virological response (SVR) in a particular patient and need for long term follow up. As long as a vaccine is not available, the HCV pandemic has to be controlled by treatment-as-prevention strategy, effective screening programs and global access to treatment.

However, there is a need of new strategies that can support decision making, predominantly in primary care. In previous decades, optical diagnostic techniques including Raman and ATR-FTIR spectroscopy have become quite popular and attained focus of scientific community to explore its diagnostic capabilities for pathogenic infection using biofluids [7], [8], [9], [10], [11], [12], [13]. Attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy is a simple analytical tool which can characterize biochemical profile without the need of an extensive sample preparation [14]. Interrogation of the sample using infrared (IR) light, helps to explain a precise biochemical fingerprint. The region 1800–900 cm−1 is known as the bio fingerprint region. By the help of machine learning algorithms, it is possible to forecast disease status based on the spectral data by means of unsupervised and supervised classification [15,16]. The aim of this study is not only to diagnose HCV infection but also to assess the non-cirrhotic/cirrhotic status of patient by using freeze dried sera samples, which helps in opting the appropriate treatment regime. For this purpose, we used Principal component analysis (PCA) as an unsupervised classification algorithm. For supervised classification, we used Linear discriminant analysis (LDA), Quadratic discriminant analysis (QDA) and Support vector machine (SVM). This approach will be cost-effective, less time consuming and less traumatic for patients; as it surpasses the need for multiple screening tests (Anti-HCV ELISA, PCR, CBC, LFTs, USG, SWE and CT-scan) which are used conventionally to reach a conclusion regarding the non-cirrhotic/cirrhotic status of an infected individual. Though these tests are being used in routine but need trained handlers and multiple reactions/equipments which are expensive and time-consuming. As successive series of tests is reliant on the results of the preceding and therefore need multiple visits making the process cumbersome.