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:

A protocol for the classification of wet mass in extrusion-spheronization. In this study, a structured protocol for the classification of wet mass in extrusion-spheronization was developed to predict formation and pellet quality. The wet masses of 120 formulae were prepared taking microcrystalline celluloses as pelletization aid and lactose, hydroxypropyl methylcellulose grades, herbal medicines as model drugs. Physical properties of the wet masses such as hardness, adhesiveness, springiness, cohesiveness, chewiness, and resilience were tested, respectively, using a texture analyzer. Particles were produced by spheronization process, and the quality of spherical pellets was also evaluated. Data were analyzed by principal component analysis, factor analysis, and classification analysis. The wet masses could be classified into five groups taking the ratio of hardness to springiness (Ha/Sp) as the first classification index and chewiness, resilience as the second and the third classification index. The wet masses of different classification could correspondingly form the different shapes. So, a new protocol could be devised, for example, if the range of Ha/Sp of the wet masses was 30,992-47,689g, at the same time, the value of chewiness was less than 4842, and the value of resilience was no more than 0.139; it would form spherical pellets under the experimental condition. These results demonstrate that the proposed protocol could be a valuable asset in a formulation development project to assess the physical properties of wet masses and to predict formation and pellet quality. So, the tedious and expensive pre-production (pre-formulation and optimization) work could be considerably reduced.
{"Chemical": ["lactose", "hydroxypropyl"]}