You will be shown an abstract from a biomedical research paper. Given this abstract, your task is to extract all unique entities of the following types: ["Chemical"].

Please return the output as a JSON object of the format: {"Chemical": ["Arsenic", ...]}. The keys should be entity types and values should be lists of extracted entities belonging to the corresponding type. If you cannot find entities belonging to a specific type, the value should be [].

Only output the JSON object and do not include any additional text.

Abstract:

Determination of tartrazine in beverage samples by stopped-flow analysis and three-way multivariate calibration of non-linear kinetic-spectrophotometric data. The performance of MCR-ALS was studied in the modeling of non-linear kinetic-spectrophotometric data acquired by a stopped-flow system for the quantitation of tartrazine in the presence of brilliant blue and sunset yellow FCF as possible interferents. In the present work, MCR-ALS and U-PCA/RBL were firstly applied to remove the contribution of unexpected components not included in the calibration set. Secondly, a polynomial function was used to model the non-linear data obtained by the implementation of the algorithms. MCR-ALS was the only strategy that allowed the determination of tartrazine in test samples accurately. Therefore, it was applied for the analysis of tartrazine in beverage samples with minimum sample preparation and short analysis time. The proposed method was validated by comparison with a chromatographic procedure published in the literature. Mean recovery values between 98% and 100% and relative errors of prediction values between 4% and 9% were indicative of the good performance of the method.
{"Chemical": ["brilliant blue and sunset yellow FCF", "tartrazine"]}