With the increase of expectation for higher quality of life, consumers have higher demands for quality food. Food authentication is the technical means of ensuring food quality. One approach to food authentication is Near Infrared Spectroscopy which, for instance, can be used to differentiate between organic and non-organic apples. It is effective but time-consuming and expensive. This paper presents a novel approach where low-cost hardware devices are used to collect apple images by using smartphone combined with pattern approach. We using a smartphone to obtain the apple image, the color always changes over time during the processing of the acquisition, and record the image during the color change. We convert the image into a feature vector in RGB space so that can be analyzed in some pattern recognition algorithm. In this paper we use Partial least squares discriminant analysis (PLS-DA), k-nearest neighbors (KNN) and support vector machine (SVM) to analyze the data. Experiments were carried out on a reasonable collection of apple samples and cross validation was used, resulting in an accuracy of around 90% between organic and non-organic apples. Our studies conclude that this approach has the potential to lead to a viable solution to empower consumers in food authentication.